import os
import gc
import gradio as gr
import numpy as np
import torch
import json
import spaces
import config
import utils
import logging
from PIL import Image, PngImagePlugin
from datetime import datetime
from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline


"""
Changes to base animagine-xl-3.1 log 
- Cut the wildcard 
- add in lora pipeline 
- use let get env variable
- add in lora strenght variable

"""

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

DESCRIPTION = "Animagine XL 3.1 X Galverse MAMA "
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>"
IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1"
HF_TOKEN = os.getenv("HF_TOKEN")
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
MIN_IMAGE_SIZE = 512
MAX_IMAGE_SIZE = 2048
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs")

MODEL = os.getenv(
    "MODEL",
    "https://huggingface.co/cagliostrolab/animagine-xl-3.1/blob/main/animagine-xl-3.1.safetensors",
)
LORA_MODEL ="galverse/mama-1.5"

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")



def load_pipeline(model_name):
    vae = AutoencoderKL.from_pretrained(
        "madebyollin/sdxl-vae-fp16-fix",
        torch_dtype=torch.float16,
    )
    pipeline = (
        StableDiffusionXLPipeline.from_single_file
        if MODEL.endswith(".safetensors")
        else StableDiffusionXLPipeline.from_pretrained
    )

    pipe = pipeline(
        model_name,
        vae=vae,
        torch_dtype=torch.float16,
        custom_pipeline="lpw_stable_diffusion_xl",
        use_safetensors=True,
        add_watermarker=False,
        use_auth_token=HF_TOKEN,
    )
    
    pipe.unload_lora_weights()
    pipe.load_lora_weights(LORA_MODEL ) #Add in lora to pipe 

    pipe.to(device)
    return pipe


@spaces.GPU
def generate(
    prompt: str,
    negative_prompt: str = "",
    lora_scale: float = 1.0,  #added in lora strength 
    seed: int = 0,
    custom_width: int = 1024,
    custom_height: int = 1024,
    guidance_scale: float = 7.0,
    num_inference_steps: int = 28,
    sampler: str = "Euler a",
    aspect_ratio_selector: str = "896 x 1152",
    style_selector: str = "(None)",
    quality_selector: str = "Standard v3.1",
    use_upscaler: bool = False,
    upscaler_strength: float = 0.55,
    upscale_by: float = 1.5,
    add_quality_tags: bool = True,
    progress=gr.Progress(track_tqdm=True),
):
    generator = utils.seed_everything(seed)

    width, height = utils.aspect_ratio_handler(
        aspect_ratio_selector,
        custom_width,
        custom_height,
    )

  

    prompt, negative_prompt = utils.preprocess_prompt(
        quality_prompt, quality_selector, prompt, negative_prompt, add_quality_tags
    )
    prompt, negative_prompt = utils.preprocess_prompt(
        styles, style_selector, prompt, negative_prompt
    )

    width, height = utils.preprocess_image_dimensions(width, height)

    backup_scheduler = pipe.scheduler
    pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler)

    if use_upscaler:
        upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components)
    metadata = {
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "resolution": f"{width} x {height}",
        "guidance_scale": guidance_scale,
        "num_inference_steps": num_inference_steps,
        "seed": seed,
        "sampler": sampler,
        "sdxl_style": style_selector,
        "add_quality_tags": add_quality_tags,
        "quality_tags": quality_selector,
    }
    #add in lora 
    metadata["cross_attention_kwargs"] = {"scale": lora_scale}

    if use_upscaler:
        new_width = int(width * upscale_by)
        new_height = int(height * upscale_by)
        metadata["use_upscaler"] = {
            "upscale_method": "nearest-exact",
            "upscaler_strength": upscaler_strength,
            "upscale_by": upscale_by,
            "new_resolution": f"{new_width} x {new_height}",
        }
    else:
        metadata["use_upscaler"] = None
        metadata["Model"] = {
            "Model": DESCRIPTION,
            "Model hash": "e3c47aedb0",
        }
    
    logger.info(json.dumps(metadata, indent=4))

    try:
        if use_upscaler:
            latents = pipe(
                prompt=prompt,
                negative_prompt=negative_prompt,
                width=width,
                height=height,
                guidance_scale=guidance_scale,
                num_inference_steps=num_inference_steps,
                generator=generator,
                cross_attention_kwargs={"scale":  lora_scale}, #add in lora scale setting 
                output_type="latent",
            ).images
            upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by)
            images = upscaler_pipe(
                prompt=prompt,
                negative_prompt=negative_prompt,
                image=upscaled_latents,
                guidance_scale=guidance_scale,
                num_inference_steps=num_inference_steps,
                strength=upscaler_strength,
                generator=generator,
                output_type="pil",
            ).images
        else:
            images = pipe(
                prompt=prompt,
                negative_prompt=negative_prompt,
                width=width,
                height=height,
                guidance_scale=guidance_scale,
                num_inference_steps=num_inference_steps,
                generator=generator,
                cross_attention_kwargs={"scale":  lora_scale}, #add in lora scale setting 
                output_type="pil",
            ).images

        if images:
            image_paths = [
                utils.save_image(image, metadata, OUTPUT_DIR, IS_COLAB)
                for image in images
            ]

            for image_path in image_paths:
                logger.info(f"Image saved as {image_path} with metadata")

        return image_paths, metadata
    except Exception as e:
        logger.exception(f"An error occurred: {e}")
        raise
    finally:
        if use_upscaler:
            del upscaler_pipe
        pipe.scheduler = backup_scheduler
        utils.free_memory()


if torch.cuda.is_available():
    pipe = load_pipeline(MODEL)
    logger.info("Loaded on Device!")
else:
    pipe = None

styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.style_list}
quality_prompt = {
    k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.quality_prompt_list
}



with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") as demo:
    title = gr.HTML(
        f"""<h1><span>{DESCRIPTION}</span></h1>""",
        elem_id="title",
    )
    gr.Markdown(
        f"""Gradio demo for [cagliostrolab/animagine-xl-3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1)""",
        elem_id="subtitle",
    )
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    with gr.Row():
        with gr.Column(scale=2):
            with gr.Tab("Txt2img"):
                with gr.Group():
                    prompt = gr.Text(
                        label="Prompt",
                        max_lines=5,
                        placeholder="Enter your prompt",
                    )
                    negative_prompt = gr.Text(
                        label="Negative Prompt",
                        max_lines=5,
                        placeholder="Enter a negative prompt",
                    )
                    lora_scale = gr.Slider(
                            label="lora strength",
                            minimum=0,
                            maximum=1.2,
                            step=0.1,
                            value=1,
                    )
                    with gr.Accordion(label="Quality Tags", open=True):
                        add_quality_tags = gr.Checkbox(
                            label="Add Quality Tags", value=True
                        )
                        quality_selector = gr.Dropdown(
                            label="Quality Tags Presets",
                            interactive=True,
                            choices=list(quality_prompt.keys()),
                            value="Standard v3.1",
                        )
            with gr.Tab("Advanced Settings"):
                with gr.Group():
                    style_selector = gr.Radio(
                        label="Style Preset",
                        container=True,
                        interactive=True,
                        choices=list(styles.keys()),
                        value="(None)",
                    )
                with gr.Group():
                    aspect_ratio_selector = gr.Radio(
                        label="Aspect Ratio",
                        choices=config.aspect_ratios,
                        value="896 x 1152",
                        container=True,
                    )
                with gr.Group(visible=False) as custom_resolution:
                    with gr.Row():
                        custom_width = gr.Slider(
                            label="Width",
                            minimum=MIN_IMAGE_SIZE,
                            maximum=MAX_IMAGE_SIZE,
                            step=8,
                            value=1024,
                        )
                        custom_height = gr.Slider(
                            label="Height",
                            minimum=MIN_IMAGE_SIZE,
                            maximum=MAX_IMAGE_SIZE,
                            step=8,
                            value=1024,
                        )
                with gr.Group():
                    use_upscaler = gr.Checkbox(label="Use Upscaler", value=False)
                    with gr.Row() as upscaler_row:
                        upscaler_strength = gr.Slider(
                            label="Strength",
                            minimum=0,
                            maximum=1,
                            step=0.05,
                            value=0.55,
                            visible=False,
                        )
                        upscale_by = gr.Slider(
                            label="Upscale by",
                            minimum=1,
                            maximum=1.5,
                            step=0.1,
                            value=1.5,
                            visible=False,
                        )
                with gr.Group():
                    sampler = gr.Dropdown(
                        label="Sampler",
                        choices=config.sampler_list,
                        interactive=True,
                        value="Euler a",
                    )
                with gr.Group():
                    seed = gr.Slider(
                        label="Seed", minimum=0, maximum=utils.MAX_SEED, step=1, value=0
                    )
                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                with gr.Group():
                    with gr.Row():
                        guidance_scale = gr.Slider(
                            label="Guidance scale",
                            minimum=1,
                            maximum=12,
                            step=0.1,
                            value=7.0,
                        )
                        num_inference_steps = gr.Slider(
                            label="Number of inference steps",
                            minimum=1,
                            maximum=50,
                            step=1,
                            value=28,
                        )
        with gr.Column(scale=3):
            with gr.Blocks():
                run_button = gr.Button("Generate", variant="primary")
            result = gr.Gallery(
                label="Result", 
                columns=1, 
                height='100%', 
                preview=True, 
                show_label=False
            )
            with gr.Accordion(label="Generation Parameters", open=False):
                gr_metadata = gr.JSON(label="metadata", show_label=False)
            gr.Examples(
                examples=config.examples,
                inputs=prompt,
                outputs=[result, gr_metadata],
                fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs),
                cache_examples=CACHE_EXAMPLES,
            )
    use_upscaler.change(
        fn=lambda x: [gr.update(visible=x), gr.update(visible=x)],
        inputs=use_upscaler,
        outputs=[upscaler_strength, upscale_by],
        queue=False,
        api_name=False,
    )
    aspect_ratio_selector.change(
        fn=lambda x: gr.update(visible=x == "Custom"),
        inputs=aspect_ratio_selector,
        outputs=custom_resolution,
        queue=False,
        api_name=False,
    )

    gr.on(
        triggers=[
            prompt.submit,
            negative_prompt.submit,
            run_button.click,
        ],
        fn=utils.randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=[
            prompt,
            negative_prompt,
            lora_scale,
            seed,
            custom_width,
            custom_height,
            guidance_scale,
            num_inference_steps,
            sampler,
            aspect_ratio_selector,
            style_selector,
            quality_selector,
            use_upscaler,
            upscaler_strength,
            upscale_by,
            add_quality_tags,
        ],
        outputs=[result, gr_metadata],
        api_name="run",
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)