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import gradio as gr
import torch
import os
import json
import random
import sys
import logging
import warnings
import re
from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler
from transformers import AutoModel, AutoTokenizer
from dataclasses import dataclass

sys.path.append(os.path.dirname(os.path.abspath(__file__)))

from diffusers import ZImagePipeline
from diffusers.models.transformers.transformer_z_image import ZImageTransformer2DModel
from pe import prompt_template


# ==================== Environment Variables ================================
MODEL_PATH = os.environ.get("MODEL_PATH", "Tongyi-MAI/Z-Image-Turbo")
ENABLE_COMPILE = os.environ.get("ENABLE_COMPILE", "true").lower() == "true"
ENABLE_WARMUP = os.environ.get("ENABLE_WARMUP", "true").lower() == "true"
ATTENTION_BACKEND = os.environ.get("ATTENTION_BACKEND", "_flash_3")
DASHSCOPE_API_KEY = os.environ.get("DASHSCOPE_API_KEY")
# ===========================================================================


os.environ["TOKENIZERS_PARALLELISM"] = "false"
warnings.filterwarnings("ignore")
logging.getLogger("transformers").setLevel(logging.ERROR)

RESOLUTION_SET = [
    "1024x1024 ( 1:1 )",
    "1152x896  ( 9:7 )",
    "896x1152  ( 7:9 )",
    "1152x864  ( 4:3 )",
    "864x1152  ( 3:4 )",
    "1248x832  ( 3:2 )",
    "832x1248  ( 2:3 )",
    "1280x720  (16:9 )",
    "720x1280  (9:16 )",
    "1344x576  (21:9 )",
    "576x1344  (9:21 )",
]

RES_CHOICES = {
    "1024": [
        "1024x1024 ( 1:1 )",
        "1152x896 ( 9:7 )",
        "896x1152 ( 7:9 )",
        "1152x864 ( 4:3 )",
        "864x1152 ( 3:4 )",
        "1248x832 ( 3:2 )",
        "832x1248 ( 2:3 )",
        "1280x720 ( 16:9 )",
        "720x1280 ( 9:16 )",
        "1344x576 ( 21:9 )",
        "576x1344 ( 9:21 )",
    ],
}

def get_resolution(resolution):
    match = re.search(r"(\d+)\s*[×x]\s*(\d+)", resolution)
    if match:
        return int(match.group(1)), int(match.group(2))
    return 1024, 1024

def load_models(model_path, enable_compile=False, attention_backend="native"):
    print(f"Loading models from {model_path}...")
    if not os.path.exists(model_path):
        raise FileNotFoundError(f"Model directory not found: {model_path}")

    vae = AutoencoderKL.from_pretrained(
        os.path.join(model_path, "vae"), 
        torch_dtype=torch.bfloat16, 
        device_map="cuda"
    )
    
    text_encoder = AutoModel.from_pretrained(
        os.path.join(model_path, "text_encoder"),
        torch_dtype=torch.bfloat16,
        device_map="cuda",
    ).eval()
    
    tokenizer = AutoTokenizer.from_pretrained(os.path.join(model_path, "tokenizer"))
    tokenizer.padding_side = "left"
    
    if enable_compile:
        print("Enabling torch.compile optimizations...")
        torch._inductor.config.conv_1x1_as_mm = True
        torch._inductor.config.coordinate_descent_tuning = True
        torch._inductor.config.epilogue_fusion = False
        torch._inductor.config.coordinate_descent_check_all_directions = True
        torch._inductor.config.max_autotune_gemm = True
        torch._inductor.config.max_autotune_gemm_backends = "TRITON,ATEN"
        torch._inductor.config.triton.cudagraphs = False

    pipe = ZImagePipeline(
        scheduler=None,
        vae=vae,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        transformer=None
    )
    
    if enable_compile:
        pipe.vae.disable_tiling()
    
    transformer = ZImageTransformer2DModel.from_pretrained(
        os.path.join(model_path, "transformer")
    ).to("cuda", torch.bfloat16)
    
    pipe.transformer = transformer
    pipe.transformer.set_attention_backend(attention_backend)

    if enable_compile:
        print("Compiling transformer...")
        pipe.transformer = torch.compile(
            pipe.transformer, mode="max-autotune-no-cudagraphs", fullgraph=False
        )

    pipe.to("cuda", torch.bfloat16)
    
    return pipe

def generate_image(
    pipe,
    prompt,
    resolution="1024x1024",
    seed=-1,
    guidance_scale=5.0,
    num_inference_steps=50,
    shift=3.0,
    max_sequence_length=512,
):
    height, width = get_resolution(resolution)
    
    if seed == -1:
        seed = torch.randint(0, 1000000, (1,)).item()
    print(f"Using seed: {seed}")
    
    generator = torch.Generator("cuda").manual_seed(seed)
    
    scheduler = FlowMatchEulerDiscreteScheduler(
        num_train_timesteps=1000, 
        shift=shift
    )
    pipe.scheduler = scheduler
    
    image = pipe(
        prompt=prompt,
        height=height,
        width=width,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
        max_sequence_length=max_sequence_length,
    ).images[0]
    
    return image

def warmup_model(pipe, resolutions):
    print("Starting warmup phase...")
    
    dummy_prompt = "warmup"
    
    for res_str in resolutions:
        print(f"Warming up for resolution: {res_str}")
        try:
            for i in range(3):
                generate_image(
                    pipe,
                    prompt=dummy_prompt,
                    resolution=res_str,
                    num_inference_steps=9,
                    guidance_scale=0.0,
                    seed=42 + i
                )
        except Exception as e:
            print(f"Warmup failed for {res_str}: {e}")
            
    print("Warmup completed.")

# ==================== Prompt Expander ====================
@dataclass
class PromptOutput:
    status: bool
    prompt: str
    seed: int
    system_prompt: str
    message: str

class PromptExpander:
    def __init__(self, backend="api", **kwargs):
        self.backend = backend

    def decide_system_prompt(self, template_name=None):
        return prompt_template

class APIPromptExpander(PromptExpander):
    def __init__(self, api_config=None, **kwargs):
        super().__init__(backend="api", **kwargs)
        self.api_config = api_config or {}
        self.client = self._init_api_client()

    def _init_api_client(self):
        try:
            from openai import OpenAI
            api_key = self.api_config.get("api_key") or DASHSCOPE_API_KEY
            base_url = self.api_config.get("base_url", "https://dashscope.aliyuncs.com/compatible-mode/v1")
            
            if not api_key:
                print("Warning: DASHSCOPE_API_KEY not found.")
                return None

            return OpenAI(api_key=api_key, base_url=base_url)
        except ImportError:
            print("Please install openai: pip install openai")
            return None
        except Exception as e:
            print(f"Failed to initialize API client: {e}")
            return None

    def __call__(self, prompt, system_prompt=None, seed=-1, **kwargs):
        return self.extend(prompt, system_prompt, seed, **kwargs)

    def extend(self, prompt, system_prompt=None, seed=-1, **kwargs):
        if self.client is None:
            return PromptOutput(False, "", seed, system_prompt, "API client not initialized")

        if system_prompt is None:
            system_prompt = self.decide_system_prompt()

        if "{prompt}" in system_prompt:
            system_prompt = system_prompt.format(prompt=prompt)
            prompt = " " 

        try:
            model = self.api_config.get("model", "qwen3-max-preview")
            response = self.client.chat.completions.create(
                model=model,
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.7,
                top_p=0.8,
            )
            
            content = response.choices[0].message.content
            json_start = content.find("```json")
            if json_start != -1:
                 json_end = content.find("```", json_start + 7)
                 try:
                     json_str = content[json_start + 7 : json_end].strip()
                     data = json.loads(json_str)
                     expanded_prompt = data.get("revised_prompt", content)
                 except:
                     expanded_prompt = content
            else:
                expanded_prompt = content

            return PromptOutput(
                status=True,
                prompt=expanded_prompt,
                seed=seed,
                system_prompt=system_prompt,
                message=content
            )
        except Exception as e:
            return PromptOutput(False, "", seed, system_prompt, str(e))

def create_prompt_expander(backend="api", **kwargs):
    if backend == "api":
        return APIPromptExpander(**kwargs)
    raise ValueError("Only 'api' backend is supported.")

pipe = None
prompt_expander = None

def init_app():
    global pipe, prompt_expander

    try:
        pipe = load_models(MODEL_PATH, enable_compile=ENABLE_COMPILE, attention_backend=ATTENTION_BACKEND)
        print(f"Model loaded. Compile: {ENABLE_COMPILE}, Backend: {ATTENTION_BACKEND}")
        
        if ENABLE_WARMUP:
            all_resolutions = []
            for cat in RES_CHOICES.values():
                all_resolutions.extend(cat)
            warmup_model(pipe, all_resolutions)
            
    except Exception as e:
        print(f"Error loading model: {e}")
        pipe = None

    try:
        prompt_expander = create_prompt_expander(backend="api", api_config={"model": "qwen3-max-preview"})
        print("Prompt expander initialized.")
    except Exception as e:
        print(f"Error initializing prompt expander: {e}")
        prompt_expander = None

def prompt_enhance(prompt, enable_enhance):
    if not enable_enhance or not prompt_expander:
        return prompt, "Enhancement disabled or not available."
    
    if not prompt.strip():
        return "", "Please enter a prompt."

    try:
        result = prompt_expander(prompt)
        if result.status:
            return result.prompt, result.message
        else:
            return prompt, f"Enhancement failed: {result.message}"
    except Exception as e:
        return prompt, f"Error: {str(e)}"

def generate(prompt, resolution, seed, steps, shift, enhance):
    if pipe is None:
        raise gr.Error("Model not loaded.")
    
    final_prompt = prompt
    
    if enhance:
        final_prompt, _ = prompt_enhance(prompt, True)
        print(f"Enhanced prompt: {final_prompt}")

    if seed == -1:
        seed = random.randint(0, 1000000)
    
    try:
        resolution_str = resolution.split(" ")[0]
    except:
        resolution_str = "1024x1024"

    image = generate_image(
        pipe=pipe,
        prompt=final_prompt,
        resolution=resolution_str,
        seed=seed,
        guidance_scale=0.0,
        num_inference_steps=steps,
        shift=shift
    )
    
    return image, final_prompt, str(seed)

# ==================== Gradio Interface ====================
init_app()

with gr.Blocks(title="Z-Image Demo") as demo:
    gr.Markdown("# Z-Image Generation Demo")
    
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(label="Prompt", lines=3, placeholder="Enter your prompt here...")
            with gr.Row():
                enable_enhance = gr.Checkbox(label="Enhance Prompt (DashScope)", value=True)
                enhance_btn = gr.Button("Enhance Only")
            
            with gr.Row():
                choices = [int(k) for k in RES_CHOICES.keys()]
                res_cat = gr.Dropdown(value=1024, choices=choices, label="Resolution Category")
                
                initial_res_choices = RES_CHOICES["1024"]
                resolution = gr.Dropdown(
                    value=initial_res_choices[0], 
                    choices=initial_res_choices, 
                    label="Resolution"
                )
                seed = gr.Number(label="Seed", value=-1, precision=0, info="-1 for random")
            
            with gr.Row():
                steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=9, step=1)
                shift = gr.Slider(label="Shift", minimum=1.0, maximum=10.0, value=3.0, step=0.1)
            
            generate_btn = gr.Button("Generate", variant="primary")
            
        with gr.Column():
            output_image = gr.Image(label="Generated Image", format="png")
            final_prompt_output = gr.Textbox(label="Final Prompt Used", lines=3, interactive=False)
            used_seed = gr.Textbox(label="Seed Used", interactive=False)

    def update_res_choices(_res_cat):
        if str(_res_cat) in RES_CHOICES:
            res_choices = RES_CHOICES[str(_res_cat)]
        else:
            res_choices = RES_CHOICES["1024"]
        return gr.update(value=res_choices[0], choices=res_choices)

    res_cat.change(update_res_choices, inputs=res_cat, outputs=resolution)

    enhance_btn.click(
        prompt_enhance,
        inputs=[prompt_input, enable_enhance],
        outputs=[prompt_input, final_prompt_output] 
    )

    generate_btn.click(
        generate,
        inputs=[
            prompt_input, 
            resolution, 
            seed, 
            steps, 
            shift, 
            enable_enhance
        ],
        outputs=[output_image, final_prompt_output, used_seed]
    )

if __name__ == "__main__":
    demo.launch()