import os
import gc
import gradio as gr
import numpy as np
import torch
import json
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
import tags

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

DESCRIPTION = "Animagine XL 3.1"
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 = int(os.getenv("MIN_IMAGE_SIZE", "512"))
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
USE_TORCH_COMPILE = False #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",
)
VAE_MODEL = os.getenv(
    "VAE_MODEL",
    "madebyollin/sdxl-vae-fp16-fix",
)

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_model):
    vae = AutoencoderKL.from_pretrained(
        vae_model,
        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.to(device)
    return pipe


def generate(
    prompt: str,
    negative_prompt: str = "",
    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,
    add_danbooru_tags: bool = False,
    rating_tags: str = "general",
    copyright_tags_list: list[str] = [],
    character_tags_list: list[str] = [],
    general_tags: str = "",
    ban_tags: str = "",
    do_cfg: bool = False,
    cfg_scale: float = 1.5,
    negative_tags: str = "",
    total_token_length: str = "long",
    max_new_tokens: int = 128,
    min_new_tokens: int = 0,
    temperature: float = 1.0,
    top_p: float = 1.0,
    top_k: int = 20,
    num_beams: int = 1,
    # model_backend: str = "Default",
    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 = utils.add_wildcard(prompt, wildcard_files)

    generated_tags_animagine = ""
    if add_danbooru_tags:
        generated_tags_animagine = tags.add_tags(prompt, rating_tags, copyright_tags_list, character_tags_list, general_tags, ban_tags, do_cfg, cfg_scale, negative_tags, total_token_length, max_new_tokens, min_new_tokens, temperature, top_p, top_k, num_beams)
        prompt = generated_tags_animagine.strip()

    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,
    }

    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,
                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,
                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, generated_tags_animagine
    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, VAE_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
}

wildcard_files = utils.load_wildcard_files("wildcard")

COPY_ACTION_JS = """\
(inputs, _outputs) => {
  // inputs is the string value of the input_text
  if (inputs.trim() !== "") {
    navigator.clipboard.writeText(inputs);
  }
}"""

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",
                    )
                    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",
                        )
                    add_danbooru_tags = gr.Checkbox(
                        label="Add Generated Tags", value=False
                    )
            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,
                        )

# danbooru-tags-upsampler
            with gr.Tab("tags"):
                with gr.Row():
                    with gr.Column():

                        # with gr.Group(
                        #     visible=False,
                        # ):
                        #     model_backend_radio = gr.Radio(
                        #         label="Model backend",
                        #         choices=list(MODEL_BACKEND_MAP.keys()),
                        #         value="Default",
                        #         interactive=True,
                        #     )

                        with gr.Group():
                            rating_dropdown = gr.Dropdown(
                                label="Rating",
                                choices=[
                                    "general",
                                    "sensitive",
                                    "questionable",
                                    "explicit",
                                ],
                                value="general",
                            )

                            with gr.Group():
                                copyright_tags_mode_dropdown = gr.Dropdown(
                                    label="Copyright tags mode",
                                    choices=[
                                        "None",
                                        "Original",
                                        # "Auto", # TODO: implement these modes
                                        # "Random",
                                        "Custom",
                                    ],
                                    value="None",
                                    interactive=True,
                                )
                                copyright_tags_dropdown = gr.Dropdown(
                                    label="Copyright tags",
                                    choices=tags.get_copyright_tags_list(),  # type: ignore
                                    value=[],
                                    multiselect=True,
                                    visible=False,
                                )

                                def on_change_copyright_tags_dropdouwn(mode: str):
                                    kwargs: dict = {"visible": mode == "Custom"}
                                    if mode == "Original":
                                        kwargs["value"] = ["original"]
                                    elif mode == "None":
                                        kwargs["value"] = []

                                    return gr.update(**kwargs)

                            with gr.Group():
                                character_tags_mode_dropdown = gr.Dropdown(
                                    label="Character tags mode",
                                    choices=[
                                        "None",
                                        # "Auto", # TODO: implement these modes
                                        # "Random",
                                        "Custom",
                                    ],
                                    value="None",
                                    interactive=True,
                                )
                                character_tags_dropdown = gr.Dropdown(
                                    label="Character tags",
                                    choices=tags.get_character_tags_list(),  # type: ignore
                                    value=[],
                                    multiselect=True,
                                    visible=False,
                                )

                                def on_change_character_tags_dropdouwn(mode: str):
                                    kwargs: dict = {"visible": mode == "Custom"}
                                    if mode == "None":
                                        kwargs["value"] = []

                                    return gr.update(**kwargs)

                        with gr.Group():
                            general_tags_textbox = gr.Textbox(
                                label="General tags (the condition to generate tags)",
                                value="",
                                placeholder="1girl, ...",
                                lines=4,
                            )

                            ban_tags_textbox = gr.Textbox(
                                label="Ban tags (tags in this field never appear in generation)",
                                value="",
                                placeholder="official alternate cosutme, english text,...",
                                lines=2,
                            )

                        generate_btn = gr.Button("Generate", variant="primary")

                        with gr.Accordion(label="Generation config (advanced)", open=False):
                            with gr.Group():
                                do_cfg_check = gr.Checkbox(
                                    label="Do CFG (Classifier Free Guidance)",
                                    value=False,
                                )
                                cfg_scale_slider = gr.Slider(
                                    label="CFG scale",
                                    maximum=3.0,
                                    minimum=0.1,
                                    step=0.1,
                                    value=1.5,
                                    visible=False,
                                )
                                negative_tags_textbox = gr.Textbox(
                                    label="Negative prompt",
                                    placeholder="simple background, ...",
                                    value="",
                                    lines=2,
                                    visible=False,
                                )

                                def on_change_do_cfg_check(do_cfg: bool):
                                    kwargs: dict = {"visible": do_cfg}
                                    return gr.update(**kwargs), gr.update(**kwargs)

                                do_cfg_check.change(
                                    on_change_do_cfg_check,
                                    inputs=[do_cfg_check],
                                    outputs=[cfg_scale_slider, negative_tags_textbox],
                                )

                            with gr.Group():
                                total_token_length_radio = gr.Radio(
                                    label="Total token length",
                                    choices=list(tags.get_length_tags().keys()),
                                    value="long",
                                )

                            with gr.Group():
                                max_new_tokens_slider = gr.Slider(
                                    label="Max new tokens",
                                    maximum=256,
                                    minimum=1,
                                    step=1,
                                    value=128,
                                )
                                min_new_tokens_slider = gr.Slider(
                                    label="Min new tokens",
                                    maximum=255,
                                    minimum=0,
                                    step=1,
                                    value=0,
                                )
                                temperature_slider = gr.Slider(
                                    label="Temperature (larger is more random)",
                                    maximum=1.0,
                                    minimum=0.0,
                                    step=0.1,
                                    value=1.0,
                                )
                                top_p_slider = gr.Slider(
                                    label="Top p (larger is more random)",
                                    maximum=1.0,
                                    minimum=0.0,
                                    step=0.1,
                                    value=1.0,
                                )
                                top_k_slider = gr.Slider(
                                    label="Top k (larger is more random)",
                                    maximum=500,
                                    minimum=1,
                                    step=1,
                                    value=100,
                                )
                                num_beams_slider = gr.Slider(
                                    label="Number of beams (smaller is more random)",
                                    maximum=10,
                                    minimum=1,
                                    step=1,
                                    value=1,
                                )

                    with gr.Column():
                        with gr.Group():
                            output_tags_natural = gr.Textbox(
                                label="Generation result",
                                # placeholder="tags will be here",
                                interactive=False,
                            )
                            output_tags_natural_copy_btn = gr.Button("Copy", visible=False)
                            output_tags_natural_copy_btn.click(
                                fn=tags.copy_text,
                                inputs=[output_tags_natural],
                                js=COPY_ACTION_JS,
                            )

                        with gr.Group():
                            output_tags_general_only = gr.Textbox(
                                label="General tags only (sorted)",
                                interactive=False,
                            )
                            output_tags_general_only_copy_btn = gr.Button("Copy", visible=False)
                            output_tags_general_only_copy_btn.click(
                                fn=tags.copy_text,
                                inputs=[output_tags_general_only],
                                js=COPY_ACTION_JS,
                            )

                        with gr.Group():
                            output_tags_animagine = gr.Textbox(
                                label="Output tags (AnimagineXL v3 style order)",
                                # placeholder="tags will be here in Animagine v3 style order",
                                interactive=False,
                            )
                            output_tags_animagine_copy_btn = gr.Button("Copy", visible=False)
                            output_tags_animagine_copy_btn.click(
                                fn=tags.copy_text,
                                inputs=[output_tags_animagine],
                                js=COPY_ACTION_JS,
                            )

                        with gr.Accordion(label="Metadata", open=False):
                            _model_backend_md = gr.Markdown(
                                f"Model backend: {tags.get_model_backend()}",
                            )
                            input_prompt_raw = gr.Textbox(
                                label="Input prompt (raw)",
                                interactive=False,
                                lines=4,
                            )

                            output_tags_raw = gr.Textbox(
                                label="Output tags (raw)",
                                interactive=False,
                                lines=4,
                            )

                        elapsed_time_md = gr.Markdown(value="Waiting to generate...")

                    copyright_tags_mode_dropdown.change(
                        on_change_copyright_tags_dropdouwn,
                        inputs=[copyright_tags_mode_dropdown],
                        outputs=[copyright_tags_dropdown],
                    )
                    character_tags_mode_dropdown.change(
                        on_change_character_tags_dropdouwn,
                        inputs=[character_tags_mode_dropdown],
                        outputs=[character_tags_dropdown],
                    )

                    generate_btn.click(
                        tags.handle_inputs,
                        inputs=[
                            rating_dropdown,
                            copyright_tags_dropdown,
                            character_tags_dropdown,
                            general_tags_textbox,
                            ban_tags_textbox,
                            do_cfg_check,
                            cfg_scale_slider,
                            negative_tags_textbox,
                            total_token_length_radio,
                            max_new_tokens_slider,
                            min_new_tokens_slider,
                            temperature_slider,
                            top_p_slider,
                            top_k_slider,
                            num_beams_slider,
                            # model_backend_radio,
                        ],
                        outputs=[
                            output_tags_natural,
                            output_tags_general_only,
                            output_tags_animagine,
                            input_prompt_raw,
                            output_tags_raw,
                            elapsed_time_md,
                            output_tags_natural_copy_btn,
                            output_tags_general_only_copy_btn,
                            output_tags_animagine_copy_btn,
                        ],
                    )

                gr.Examples(
                    examples=[
                        ["1girl, solo, from side", ""],
                        ["1girl, solo, abstract, from above", ""],
                        ["2girls, yuri", "1boy"],
                        ["no humans, scenery, summer, day", ""],
                    ],
                    inputs=[
                        general_tags_textbox,
                        ban_tags_textbox,
                    ],
                )

        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
            )
            generated_tags_animagine = gr.Textbox(
                label="Generated tags (AnimagineXL v3 style order)",
                # placeholder="tags will be here in Animagine v3 style order",
                interactive=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,
            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,
            add_danbooru_tags,
            rating_dropdown,
            copyright_tags_dropdown,
            character_tags_dropdown,
            general_tags_textbox,
            ban_tags_textbox,
            do_cfg_check,
            cfg_scale_slider,
            negative_tags_textbox,
            total_token_length_radio,
            max_new_tokens_slider,
            min_new_tokens_slider,
            temperature_slider,
            top_p_slider,
            top_k_slider,
            num_beams_slider,
            # model_backend_radio,
        ],
        outputs=[result, gr_metadata, generated_tags_animagine],
        api_name="run",
    )

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