import gradio as gr
import numpy as np
import random
import torch
import spaces
from PIL import Image
from diffusers import FlowMatchEulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from tqdm import tqdm
import gc
import math
import os
import base64
import json

from optimization import optimize_pipeline_
from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
from lora_manager import LoRAManager

# System prompt for prompt enhancement
SYSTEM_PROMPT = '''
# Edit Instruction Rewriter
You are a professional edit instruction rewriter. Your task is to generate a precise, concise, and visually achievable professional-level edit instruction based on the user-provided instruction and the image to be edited.  

Please strictly follow the rewriting rules below:

## 1. General Principles
- Keep the rewritten prompt **concise and comprehensive**. Avoid overly long sentences and unnecessary descriptive language.  
- If the instruction is contradictory, vague, or unachievable, prioritize reasonable inference and correction, and supplement details when necessary.  
- Keep the main part of the original instruction unchanged, only enhancing its clarity, rationality, and visual feasibility.  
- All added objects or modifications must align with the logic and style of the scene in the input images.  
- If multiple sub-images are to be generated, describe the content of each sub-image individually.  

## 2. Task-Type Handling Rules

### 1. Add, Delete, Replace Tasks
- If the instruction is clear (already includes task type, target entity, position, quantity, attributes), preserve the original intent and only refine the grammar.  
- If the description is vague, supplement with minimal but sufficient details (category, color, size, orientation, position, etc.). For example:  
    > Original: "Add an animal"  
    > Rewritten: "Add a light-gray cat in the bottom-right corner, sitting and facing the camera"  
- Remove meaningless instructions: e.g., "Add 0 objects" should be ignored or flagged as invalid.  
- For replacement tasks, specify "Replace Y with X" and briefly describe the key visual features of X.  

### 2. Text Editing Tasks
- All text content must be enclosed in English double quotes `" "`. Keep the original language of the text, and keep the capitalization.  
- Both adding new text and replacing existing text are text replacement tasks, For example:  
    - Replace "xx" to "yy"  
    - Replace the mask / bounding box to "yy"  
    - Replace the visual object to "yy"  
- Specify text position, color, and layout only if user has required.  
- If font is specified, keep the original language of the font.  

### 3. Human Editing Tasks
- Make the smallest changes to the given user's prompt.  
- If changes to background, action, expression, camera shot, or ambient lighting are required, please list each modification individually.
- **Edits to makeup or facial features / expression must be subtle, not exaggerated, and must preserve the subject's identity consistency.**
    > Original: "Add eyebrows to the face"  
    > Rewritten: "Slightly thicken the person's eyebrows with little change, look natural."

### 4. Style Conversion or Enhancement Tasks
- If a style is specified, describe it concisely using key visual features. For example:  
    > Original: "Disco style"  
    > Rewritten: "1970s disco style: flashing lights, disco ball, mirrored walls, vibrant colors"  
- For style reference, analyze the original image and extract key characteristics (color, composition, texture, lighting, artistic style, etc.), integrating them into the instruction.  
- **Colorization tasks (including old photo restoration) must use the fixed template:**  
  "Restore and colorize the old photo."  
- Clearly specify the object to be modified. For example:  
    > Original: Modify the subject in Picture 1 to match the style of Picture 2.  
    > Rewritten: "Change the girl in Picture 1 to the ink-wash style of Picture 2 — rendered in black-and-white watercolor with soft color transitions.

### 5. Material Replacement
- Clearly specify the object and the material. For example: "Change the material of the apple to papercut style."
- For text material replacement, use the fixed template:
    "Change the material of text "xxxx" to laser style"

### 6. Logo/Pattern Editing
- Material replacement should preserve the original shape and structure as much as possible. For example:
   > Original: "Convert to sapphire material"  
   > Rewritten: "Convert the main subject in the image to sapphire material, preserving similar shape and structure"
- When migrating logos/patterns to new scenes, ensure shape and structure consistency. For example:
   > Original: "Migrate the logo in the image to a new scene"  
   > Rewritten: "Migrate the logo in the image to a new scene, preserving similar shape and structure"

### 7. Multi-Image Tasks
- Rewritten prompts must clearly point out which image's element is being modified. For example:  
    > Original: "Replace the subject of picture 1 with the subject of picture 2"  
    > Rewritten: "Replace the girl of picture 1 with the boy of picture 2, keeping picture 2's background unchanged"  
- For stylization tasks, describe the reference image's style in the rewritten prompt, while preserving the visual content of the source image.  

## 3. Rationale and Logic Check
- Resolve contradictory instructions: e.g., "Remove all trees but keep all trees" requires logical correction.
- Supplement missing critical information: e.g., if position is unspecified, choose a reasonable area based on composition (near subject, blank space, center/edge, etc.).

# Output Format Example
```json
{
   "Rewritten": "..."
}
```
'''

def encode_image(pil_image):
    """Encode PIL image to base64 string for API calls"""
    import io
    buffered = io.BytesIO()
    pil_image.save(buffered, format="PNG")
    return base64.b64encode(buffered.getvalue()).decode("utf-8")

def polish_prompt_hf(prompt, img_list):
    """Rewrite prompt using Hugging Face InferenceClient"""
    from huggingface_hub import InferenceClient
    
    # Ensure HF_TOKEN is set
    api_key = os.environ.get("HF_TOKEN")
    if not api_key:
        print("Warning: HF_TOKEN not set. Falling back to original prompt.")
        return prompt

    try:
        # Format the prompt for the API
        formatted_prompt = f"{SYSTEM_PROMPT}\n\nUser Input: {prompt}\n\nRewritten Prompt:"
        
        # Initialize the client
        client = InferenceClient(
            provider="novita",
            api_key=api_key,
        )

        # Format the messages for the chat completions API
        sys_prompt = "you are a helpful assistant, you should provide useful answers to users."
        
        # Create messages structure
        messages = [
            {"role": "system", "content": sys_prompt},
            {"role": "user", "content": []}
        ]
        
        # Add images to the message
        for img in img_list:
            messages[1]["content"].append(
                {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encode_image(img)}"}})
        
        # Add text to the message
        messages[1]["content"].append({"type": "text", "text": f"{formatted_prompt}"})

        completion = client.chat.completions.create(
            model="Qwen/Qwen3-Next-80B-A3B-Instruct",
            messages=messages,
        )
        
        # Parse the response
        result = completion.choices[0].message.content
        
        # Try to extract JSON if present
        if '{"Rewritten"' in result:
            try:
                # Clean up the response
                result = result.replace('```json', '').replace('```', '')
                result_json = json.loads(result)
                polished_prompt = result_json.get('Rewritten', result)
            except:
                polished_prompt = result
        else:
            polished_prompt = result
            
        polished_prompt = polished_prompt.strip().replace("\n", " ")
        return polished_prompt
        
    except Exception as e:
        print(f"Error during API call to Hugging Face: {e}")
        # Fallback to original prompt if enhancement fails
        return prompt

# Define simplified LoRA configurations with Lightning as always-loaded base
LORA_CONFIG = {
    "Lightning (4-Step)": {
        "repo_id": "lightx2v/Qwen-Image-Lightning",
        "filename": "Qwen-Image-Lightning-4steps-V2.0.safetensors",
        "type": "base",
        "method": "standard",
        "always_load": True,
        "prompt_template": "{prompt}",
        "description": "Fast 4-step generation LoRA - always loaded as base optimization.",
    },
    "None": {
        "repo_id": None,
        "filename": None,
        "type": "edit",
        "method": "none",
        "prompt_template": "{prompt}",
        "description": "Use the base Qwen-Image-Edit model with Lightning optimization.",
    },
    "Object Remover": {
        "repo_id": "valiantcat/Qwen-Image-Edit-Remover-General-LoRA",
        "filename": "qwen-edit-remover.safetensors",
        "type": "edit",
        "method": "standard",
        "prompt_template": "Remove {prompt}",
        "description": "Removes objects from an image while maintaining background consistency.",
    },
}

# Initialize LoRA Manager
print("Initializing model...")
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

# Scheduler configuration for Lightning
scheduler_config = {
    "base_image_seq_len": 256,
    "base_shift": math.log(3),
    "invert_sigmas": False,
    "max_image_seq_len": 8192,
    "max_shift": math.log(3),
    "num_train_timesteps": 1000,
    "shift": 1.0,
    "shift_terminal": None,
    "stochastic_sampling": False,
    "time_shift_type": "exponential",
    "use_beta_sigmas": False,
    "use_dynamic_shifting": True,
    "use_exponential_sigmas": False,
    "use_karras_sigmas": False,
}

# Initialize scheduler with Lightning config
scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
# Load the model pipeline
pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", torch_dtype=dtype).to(device)

# Apply the same optimizations from the first version
pipe.transformer.__class__ = QwenImageTransformer2DModel
pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())

# --- Ahead-of-time compilation ---
optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt")

# --- UI Constants and Helpers ---
MAX_SEED = np.iinfo(np.int32).max
# Load the model pipeline
pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509",
                                                 scheduler=scheduler,
                                                 torch_dtype=dtype).to(device)

# Apply model optimizations
pipe.transformer.__class__ = QwenImageTransformer2DModel
pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())

# Initialize LoRA Manager
lora_manager = LoRAManager(pipe, device)

# Always load Lightning LoRA first
LIGHTNING_LORA_NAME = "Lightning (4-Step)"
print(f"Loading always-active Lightning LoRA: {LIGHTNING_LORA_NAME}")

# Load and register Lightning LoRA
lightning_config = LORA_CONFIG[LIGHTNING_LORA_NAME]
lightning_lora_path = hf_hub_download(
    repo_id=lightning_config["repo_id"], 
    filename=lightning_config["filename"]
)

lora_manager.register_lora(LIGHTNING_LORA_NAME, lightning_lora_path, **lightning_config)
lora_manager.configure_lora(LIGHTNING_LORA_NAME, {
    "description": lightning_config["description"],
    "is_base": True
})

# Load Lightning LoRA and keep it always active
lora_manager.load_lora(LIGHTNING_LORA_NAME)
lora_manager.fuse_lora(LIGHTNING_LORA_NAME)

# Register other LoRAs (only Object Remover for testing)
for lora_name, config in LORA_CONFIG.items():
    if lora_name != LIGHTNING_LORA_NAME and config["repo_id"] is not None:
        lora_path = hf_hub_download(repo_id=config["repo_id"], filename=config["filename"])
        lora_manager.register_lora(lora_name, lora_path, **config)

original_transformer_state_dict = pipe.transformer.state_dict()
print("Base model and Lightning LoRA loaded and ready.")

def fuse_lora_manual(transformer, lora_state_dict, alpha=1.0):
    """Manual LoRA fusion method"""
    key_mapping = {}
    for key in lora_state_dict.keys():
        base_key = key.replace('diffusion_model.', '').rsplit('.lora_', 1)[0]
        if base_key not in key_mapping:
            key_mapping[base_key] = {}
        if 'lora_A' in key:
            key_mapping[base_key]['down'] = lora_state_dict[key]
        elif 'lora_B' in key:
            key_mapping[base_key]['up'] = lora_state_dict[key]

    for name, module in tqdm(transformer.named_modules(), desc="Fusing layers"):
        if name in key_mapping and isinstance(module, torch.nn.Linear):
            lora_weights = key_mapping[name]
            if 'down' in lora_weights and 'up' in lora_weights:
                device = module.weight.device
                dtype = module.weight.dtype
                lora_down = lora_weights['down'].to(device, dtype=dtype)
                lora_up = lora_weights['up'].to(device, dtype=dtype)
                merged_delta = lora_up @ lora_down
                module.weight.data += alpha * merged_delta
    return transformer

def load_and_fuse_additional_lora(lora_name):
    """
    Load an additional LoRA while keeping Lightning LoRA always active.
    This enables combining Lightning's speed with other LoRA capabilities.
    """
    config = LORA_CONFIG[lora_name]
    
    print(f"Loading additional LoRA: {lora_name} (Lightning will remain active)")
    
    # Get LoRA path from registry
    if lora_name in lora_manager.lora_registry:
        lora_path = lora_manager.lora_registry[lora_name]["lora_path"]
    else:
        print(f"LoRA {lora_name} not found in registry")
        return

    # Always keep Lightning LoRA loaded
    # Load additional LoRA without resetting to base state
    if config["method"] == "standard":
        print("Using standard loading method...")
        # Load additional LoRA without fusing (to preserve Lightning)
        pipe.load_lora_weights(lora_path, adapter_names=[lora_name])
        # Set both adapters as active
        pipe.set_adapters([LIGHTNING_LORA_NAME, lora_name])
        print(f"Lightning + {lora_name} now active.")
    elif config["method"] == "manual_fuse":
        print("Using manual fusion method...")
        lora_state_dict = load_file(lora_path)
        # Manual fusion on top of Lightning
        pipe.transformer = fuse_lora_manual(pipe.transformer, lora_state_dict)
        print(f"Lightning + {lora_name} manually fused.")
    
    gc.collect()
    torch.cuda.empty_cache()

def load_and_fuse_lora(lora_name):
    """Legacy function for backward compatibility"""
    if lora_name == LIGHTNING_LORA_NAME:
        # Lightning is already loaded, just ensure it's active
        print("Lightning LoRA is already active.")
        pipe.set_adapters([LIGHTNING_LORA_NAME])
        return
    
    load_and_fuse_additional_lora(lora_name)

# Ahead-of-time compilation with minimal memory footprint
# Use tiny images to minimize memory during compilation
optimize_pipeline_(pipe, image=[Image.new("RGB", (64, 64)), Image.new("RGB", (64, 64))], prompt="test")

print("Model compilation complete.")

@spaces.GPU(duration=60)
def infer(
    lora_name,
    input_image,
    style_image,
    prompt,
    seed,
    randomize_seed,
    true_guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    """Main inference function with Lightning always active"""
    if not lora_name:
        raise gr.Error("Please select a LoRA model.")
    
    config = LORA_CONFIG[lora_name]
    
    if config["type"] == "style":
        if style_image is None:
            raise gr.Error("Style Transfer LoRA requires a Style Reference Image.")
        image_for_pipeline = style_image
    else:  # 'edit' or 'base'
        if input_image is None:
            raise gr.Error("This LoRA requires an Input Image.")
        image_for_pipeline = input_image

    if not prompt and config["prompt_template"] != "change the face to face segmentation mask":
        raise gr.Error("A text prompt is required for this LoRA.")
    
    # Load additional LoRA while keeping Lightning active
    load_and_fuse_lora(lora_name)
    
    final_prompt = config["prompt_template"].format(prompt=prompt)
    
    if randomize_seed:
        seed = random.randint(0, np.iinfo(np.int32).max)
    generator = torch.Generator(device=device).manual_seed(int(seed))
    
    print("--- Running Inference ---")
    print(f"LoRA: {lora_name} (with Lightning always active)")
    print(f"Prompt: {final_prompt}")
    print(f"Seed: {seed}, Steps: {num_inference_steps}, CFG: {true_guidance_scale}")
    
    with torch.inference_mode():
        result_image = pipe(
            image=image_for_pipeline,
            prompt=final_prompt,
            negative_prompt=" ",
            num_inference_steps=int(num_inference_steps),
            generator=generator,
            true_cfg_scale=true_guidance_scale,
        ).images[0]
        
    # Don't unfuse Lightning - keep it active for next inference
    if lora_name != LIGHTNING_LORA_NAME:
        pipe.disable_adapters()  # Disable additional LoRA but keep Lightning
    gc.collect()
    torch.cuda.empty_cache()
    
    return result_image, seed

def on_lora_change(lora_name):
    """Dynamic UI component visibility handler"""
    config = LORA_CONFIG[lora_name]
    is_style_lora = config["type"] == "style"
    
    # Lightning LoRA info
    lightning_info = "⚡ **Lightning LoRA always active** - Fast 4-step generation enabled"
    
    return {
        lora_description: gr.Markdown(visible=True, value=f"**{lightning_info}**  \n\n**Description:** {config['description']}"),
        input_image_box: gr.Image(visible=not is_style_lora, type="pil"),
        style_image_box: gr.Image(visible=is_style_lora, type="pil"),
        prompt_box: gr.Textbox(visible=(config["prompt_template"] != "change the face to face segmentation mask"))
    }

with gr.Blocks(css="#col-container { margin: 0 auto; max-width: 1024px; }") as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML('<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_edit_logo.png" alt="Qwen-Image Logo" style="width: 400px; margin: 0 auto; display: block;">')
        gr.Markdown("<h2 style='text-align: center;'>Qwen-Image-Edit Multi-LoRA Playground</h2>")
        gr.Markdown("""
        [Learn more](https://github.com/QwenLM/Qwen-Image) about the Qwen-Image series. 
        This demo uses the new [Qwen-Image-Edit-2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) with support for multiple LoRA adapters.
        **⚡ Lightning LoRA is always active for fast 4-step generation** - combine it with Object Remover for optimized performance.
        Try on [Qwen Chat](https://chat.qwen.ai/), or [download model](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) to run locally with ComfyUI or diffusers.
        """)

        with gr.Row():
            with gr.Column(scale=1):
                lora_selector = gr.Dropdown(
                    label="Select Additional LoRA (Lightning Always Active)",
                    choices=list(LORA_CONFIG.keys()),
                    value=LIGHTNING_LORA_NAME,
                    info="Lightning LoRA provides fast 4-step generation and is always active"
                )
                lora_description = gr.Markdown(visible=False)
                
                input_image_box = gr.Image(label="Input Image", type="pil", visible=True)
                style_image_box = gr.Image(label="Style Reference Image", type="pil", visible=False)
                
                prompt_box = gr.Textbox(label="Prompt", placeholder="Describe the object to remove...")
                
                run_button = gr.Button("Generate!", variant="primary")

            with gr.Column(scale=1):
                result_image = gr.Image(label="Result", type="pil")
                used_seed = gr.Number(label="Used Seed", interactive=False)

        with gr.Accordion("Advanced Settings", open=False):
            seed_slider = gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.int32).max, step=1, value=42)
            randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True)
            cfg_slider = gr.Slider(label="Guidance Scale (CFG)", minimum=1.0, maximum=10.0, step=0.1, value=4.0)
            steps_slider = gr.Slider(label="Inference Steps", minimum=4, maximum=50, step=1, value=4, info="Optimized for Lightning's 4-step generation")

        lora_selector.change(
            fn=on_lora_change,
            inputs=lora_selector,
            outputs=[lora_description, input_image_box, style_image_box, prompt_box]
        )

        demo.load(
            fn=on_lora_change,
            inputs=lora_selector,
            outputs=[lora_description, input_image_box, style_image_box, prompt_box]
        )

        run_button.click(
            fn=infer,
            inputs=[
                lora_selector,
                input_image_box, style_image_box,
                prompt_box,
                seed_slider, randomize_seed_checkbox,
                cfg_slider, steps_slider
            ],
            outputs=[result_image, used_seed]
        )

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