import gradio as gr
import torch
from PIL import Image
import os
from transformers import CLIPTokenizer, CLIPTextModel, AutoProcessor, T5EncoderModel, T5TokenizerFast
from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler
from flux.transformer_flux import FluxTransformer2DModel
from flux.pipeline_flux_chameleon import FluxPipeline
import torch.nn as nn
import math
import logging
import sys

import os
# 设置环境变量，强制禁用 accelerate 的显存管理
os.environ["ACCELERATE_USE_MEMORY_EFFICIENT_ATTENTION"] = "false"
os.environ["ACCELERATE_DISABLE_MEMORY_EFFICIENT_ATTENTION"] = "1"
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True,garbage_collection_threshold:0.6,max_split_size_mb:512"

from qwen2_vl.modeling_qwen2_vl import Qwen2VLSimplifiedModel

# 设置日志
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout)
    ]
)
logger = logging.getLogger(__name__)

MODEL_ID = "Djrango/Qwen2vl-Flux"

# Add aspect ratio options
ASPECT_RATIOS = {
    "1:1": (1024, 1024),
    "16:9": (1344, 768),
    "9:16": (768, 1344),
    "2.4:1": (1536, 640),
    "3:4": (896, 1152),
    "4:3": (1152, 896),
}

class Qwen2Connector(nn.Module):
    def __init__(self, input_dim=3584, output_dim=4096):
        super().__init__()
        self.linear = nn.Linear(input_dim, output_dim)
    
    def forward(self, x):
        return self.linear(x)

class FluxInterface:
    def __init__(self, device="cuda" if torch.cuda.is_available() else "cpu"):
        self.device = device
        self.dtype = torch.bfloat16
        self.models = None
        self.MODEL_ID = "Djrango/Qwen2vl-Flux"
        
    def load_models(self):
        if self.models is not None:
            return

        logger.info("Starting model loading...")
        # 3. 显式设置 PyTorch 缓存分配器的行为
        torch.cuda.set_per_process_memory_fraction(0.95)  # 允许使用95%的显存
        torch.cuda.max_memory_allocated = lambda *args, **kwargs: 0  # 忽略已分配内存的限制
        
        # Load FLUX components
        tokenizer = CLIPTokenizer.from_pretrained(self.MODEL_ID, subfolder="flux/tokenizer")
        text_encoder = CLIPTextModel.from_pretrained(self.MODEL_ID, subfolder="flux/text_encoder").to(self.dtype).to(self.device)
        text_encoder_two = T5EncoderModel.from_pretrained(self.MODEL_ID, subfolder="flux/text_encoder_2").to(self.dtype).to(self.device)
        tokenizer_two = T5TokenizerFast.from_pretrained(self.MODEL_ID, subfolder="flux/tokenizer_2")
        
        # Load VAE and transformer
        vae = AutoencoderKL.from_pretrained(self.MODEL_ID, subfolder="flux/vae").to(self.dtype).to(self.device)
        transformer = FluxTransformer2DModel.from_pretrained(self.MODEL_ID, subfolder="flux/transformer").to(self.dtype).to(self.device)
        scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(self.MODEL_ID, subfolder="flux/scheduler", shift=1)
        
        # Load Qwen2VL components
        qwen2vl = Qwen2VLSimplifiedModel.from_pretrained(self.MODEL_ID, subfolder="qwen2-vl").to(self.dtype).to(self.device)
        
        # Load connector
        connector = Qwen2Connector().to(self.dtype).to(self.device)
        connector_path = f"https://huggingface.co./{self.MODEL_ID}/resolve/main/qwen2-vl/connector.pt"
        connector_state = torch.hub.load_state_dict_from_url(connector_path, map_location='cpu')
        # Move state dict to dtype before loading
        connector_state = {k: v.to(self.dtype) for k, v in connector_state.items()}
        connector.load_state_dict(connector_state)
        connector = connector.to(self.device)
        
        # Load T5 embedder
        self.t5_context_embedder = nn.Linear(4096, 3072).to(self.dtype).to(self.device)
        t5_embedder_path = f"https://huggingface.co./{self.MODEL_ID}/resolve/main/qwen2-vl/t5_embedder.pt"
        t5_embedder_state = torch.hub.load_state_dict_from_url(t5_embedder_path, map_location='cpu')
        # Move state dict to dtype before loading
        t5_embedder_state = {k: v.to(self.dtype) for k, v in t5_embedder_state.items()}
        self.t5_context_embedder.load_state_dict(t5_embedder_state)
        self.t5_context_embedder = self.t5_context_embedder.to(self.device)
        
        # Set models to eval mode
        for model in [text_encoder, text_encoder_two, vae, transformer, qwen2vl, connector, self.t5_context_embedder]:
            model.requires_grad_(False)
            model.eval()
        
        logger.info("All models loaded successfully")
        
        self.models = {
            'tokenizer': tokenizer,
            'text_encoder': text_encoder,
            'text_encoder_two': text_encoder_two,
            'tokenizer_two': tokenizer_two,
            'vae': vae,
            'transformer': transformer,
            'scheduler': scheduler,
            'qwen2vl': qwen2vl,
            'connector': connector
        }
        
        # Initialize processor and pipeline
        self.qwen2vl_processor = AutoProcessor.from_pretrained(
            self.MODEL_ID, 
            subfolder="qwen2-vl",
            min_pixels=256*28*28, 
            max_pixels=256*28*28
        )
        
        self.pipeline = FluxPipeline(
            transformer=transformer,
            scheduler=scheduler,
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
        ) 
    
    def resize_image(self, img, max_pixels=1050000):
        if not isinstance(img, Image.Image):
            img = Image.fromarray(img)
        
        width, height = img.size
        num_pixels = width * height
        
        if num_pixels > max_pixels:
            scale = math.sqrt(max_pixels / num_pixels)
            new_width = int(width * scale)
            new_height = int(height * scale)
            new_width = new_width - (new_width % 8)
            new_height = new_height - (new_height % 8)
            img = img.resize((new_width, new_height), Image.LANCZOS)
        
        return img

    # [Previous methods remain unchanged...]
    def process_image(self, image):
        message = [
            {
                "role": "user",
                "content": [
                    {"type": "image", "image": image},
                    {"type": "text", "text": "Describe this image."},
                ]
            }
        ]
        text = self.qwen2vl_processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True)

        with torch.no_grad():
            inputs = self.qwen2vl_processor(text=[text], images=[image], padding=True, return_tensors="pt").to(self.device)
            output_hidden_state, image_token_mask, image_grid_thw = self.models['qwen2vl'](**inputs)
            image_hidden_state = output_hidden_state[image_token_mask].view(1, -1, output_hidden_state.size(-1))
            image_hidden_state = self.models['connector'](image_hidden_state)

        return image_hidden_state, image_grid_thw
    
    def compute_t5_text_embeddings(self, prompt):
        """Compute T5 embeddings for text prompt"""
        if prompt == "":
            return None
            
        text_inputs = self.models['tokenizer_two'](
            prompt,
            padding="max_length",
            max_length=256,
            truncation=True,
            return_tensors="pt"
        ).to(self.device)
        
        prompt_embeds = self.models['text_encoder_two'](text_inputs.input_ids)[0]
        prompt_embeds = prompt_embeds.to(dtype=self.dtype, device=self.device)
        prompt_embeds = self.t5_context_embedder(prompt_embeds)
        
        return prompt_embeds

    def compute_text_embeddings(self, prompt=""):
        with torch.no_grad():
            text_inputs = self.models['tokenizer'](
                prompt,
                padding="max_length",
                max_length=77,
                truncation=True,
                return_tensors="pt"
            ).to(self.device)

            prompt_embeds = self.models['text_encoder'](
                text_inputs.input_ids,
                output_hidden_states=False
            )
            pooled_prompt_embeds = prompt_embeds.pooler_output.to(self.dtype)

        return pooled_prompt_embeds

    def generate(self, input_image, prompt="", guidance_scale=3.5, num_inference_steps=28, num_images=2, seed=None, aspect_ratio="1:1"):
        try:
            logger.info(f"Starting generation with prompt: {prompt}, guidance_scale: {guidance_scale}, steps: {num_inference_steps}")
            
            if input_image is None:
                raise ValueError("No input image provided")
                
            if seed is not None:
                torch.manual_seed(seed)
                logger.info(f"Set random seed to: {seed}")
                
            self.load_models()
            logger.info("Models loaded successfully")
            
            # Get dimensions from aspect ratio
            if aspect_ratio not in ASPECT_RATIOS:
                raise ValueError(f"Invalid aspect ratio. Choose from {list(ASPECT_RATIOS.keys())}")
            width, height = ASPECT_RATIOS[aspect_ratio]
            logger.info(f"Using dimensions: {width}x{height}")
            
            # Process input image
            try:
                input_image = self.resize_image(input_image)
                logger.info(f"Input image resized to: {input_image.size}")
                qwen2_hidden_state, image_grid_thw = self.process_image(input_image)
                logger.info("Input image processed successfully")
            except Exception as e:
                raise RuntimeError(f"Error processing input image: {str(e)}")
            
            try:
                pooled_prompt_embeds = self.compute_text_embeddings("")
                logger.info("Base text embeddings computed")
                
                # Get T5 embeddings if prompt is provided
                t5_prompt_embeds = self.compute_t5_text_embeddings(prompt)
                logger.info("T5 prompt embeddings computed")
            except Exception as e:
                raise RuntimeError(f"Error computing embeddings: {str(e)}")
            
            # Generate images
            try:
                output_images = self.pipeline(
                    prompt_embeds=qwen2_hidden_state.repeat(num_images, 1, 1),
                    pooled_prompt_embeds=pooled_prompt_embeds,
                    t5_prompt_embeds=t5_prompt_embeds.repeat(num_images, 1, 1) if t5_prompt_embeds is not None else None,
                    num_inference_steps=num_inference_steps,
                    guidance_scale=guidance_scale,
                    height=height,
                    width=width,
                ).images
                
                logger.info("Images generated successfully")
                return output_images
            except Exception as e:
                raise RuntimeError(f"Error generating images: {str(e)}")
        except Exception as e:
            logger.error(f"Error during generation: {str(e)}")
            raise gr.Error(f"Generation failed: {str(e)}")

# Initialize the interface
interface = FluxInterface()

# Create Gradio interface
with gr.Blocks(
    theme=gr.themes.Soft(),
    css="""
        .container { 
            max-width: 1200px; 
            margin: auto; 
            padding: 0 20px;
        }
        .header { 
            text-align: center; 
            margin: 20px 0 40px 0;
            padding: 20px;
            background: #f7f7f7;
            border-radius: 12px;
        }
        .param-row {
            padding: 10px 0;
        }
        footer {
            margin-top: 40px;
            padding: 20px;
            border-top: 1px solid #eee;
        }
    """
) as demo:
    with gr.Column(elem_classes="container"):
        gr.Markdown(
            """
            <div class="header">
                # 🎨 Qwen2vl-Flux Image Variation Demo
                Generate creative variations of your images with optional text guidance
            </div>
            """
        )
        
        with gr.Row(equal_height=True):
            with gr.Column(scale=1):
                # Input Section
                input_image = gr.Image(
                    label="Upload Your Image",
                    type="pil",
                    height=384,
                    sources=["upload", "clipboard"]
                )
                
                with gr.Accordion("Advanced Settings", open=False):
                    with gr.Group():
                        prompt = gr.Textbox(
                            label="Text Prompt (Optional)",
                            placeholder="As Long As Possible...",
                            lines=3
                        )
                        
                        with gr.Row(elem_classes="param-row"):
                            guidance = gr.Slider(
                                minimum=1,
                                maximum=10,
                                value=3.5,
                                step=0.5,
                                label="Guidance Scale",
                                info="Higher values follow prompt more closely"
                            )
                            steps = gr.Slider(
                                minimum=1,
                                maximum=50,
                                value=28,
                                step=1,
                                label="Sampling Steps",
                                info="More steps = better quality but slower"
                            )
                            
                        with gr.Row(elem_classes="param-row"):
                            num_images = gr.Slider(
                                minimum=1,
                                maximum=4,
                                value=2,
                                step=1,
                                label="Number of Images",
                                info="Generate multiple variations at once"
                            )
                            seed = gr.Number(
                                label="Random Seed",
                                value=None,
                                precision=0,
                                info="Set for reproducible results"
                            )
                            aspect_ratio = gr.Radio(
                                label="Aspect Ratio",
                                choices=["1:1", "16:9", "9:16", "2.4:1", "3:4", "4:3"],
                                value="1:1",
                                info="Choose aspect ratio for generated images"
                            )
                
                submit_btn = gr.Button(
                    "🎨 Generate Variations",
                    variant="primary",
                    size="lg"
                )
            
            with gr.Column(scale=1):
                # Output Section
                output_gallery = gr.Gallery(
                    label="Generated Variations",
                    columns=2,
                    rows=2,
                    height=700,
                    object_fit="contain",
                    show_label=True,
                    allow_preview=True,
                    preview=True
                )
                error_message = gr.Textbox(visible=False)
        
        with gr.Row(elem_classes="footer"):
            gr.Markdown("""
                ### Tips:
                - 📸 Upload any image to get started
                - 💡 Add an optional text prompt to guide the generation
                - 🎯 Adjust guidance scale to control prompt influence
                - ⚙️ Increase steps for higher quality
                - 🎲 Use seeds for reproducible results
            """)
    
    # Set up the generation function
    def generate_with_error_handling(*args):
        try:
            logger.info("Starting image generation with args: %s", str(args))
            
            # 输入参数验证
            input_image, prompt, guidance, steps, num_images, seed, aspect_ratio = args
            logger.info(f"Input validation - Image: {type(input_image)}, Prompt: '{prompt}', "
                       f"Guidance: {guidance}, Steps: {steps}, Num Images: {num_images}, "
                       f"Seed: {seed}, Aspect Ratio: {aspect_ratio}")
            
            if input_image is None:
                raise ValueError("No input image provided")
            
            gr.Info("Starting image generation...")
            results = interface.generate(*args)
            logger.info("Generation completed successfully")
            gr.Info("Generation complete!")
            return [results, None]
        except Exception as e:
            error_msg = str(e)
            logger.error(f"Error in generate_with_error_handling: {error_msg}", exc_info=True)
            return [None, error_msg]

    submit_btn.click(
        fn=generate_with_error_handling,
        inputs=[
            input_image,
            prompt,
            guidance,
            steps,
            num_images,
            seed,
            aspect_ratio
        ],
        outputs=[
            output_gallery,
            error_message
        ],
        show_progress=True
    )

# Launch the app
if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",  # Listen on all network interfaces
        server_port=7860,       # Use a specific port
        share=False             # Disable public URL sharing
    )