import gradio as gr
import asyncio
from threading import RLock
from pathlib import Path


lock = RLock()
loaded_models = {}
model_info_dict = {}


def to_list(s):
    return [x.strip() for x in s.split(",")]


def list_sub(a, b):
    return [e for e in a if e not in b]


def list_uniq(l):
        return sorted(set(l), key=l.index)


def is_repo_name(s):
    import re
    return re.fullmatch(r'^[^/]+?/[^/]+?$', s)


def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30):
    from huggingface_hub import HfApi
    api = HfApi()
    default_tags = ["diffusers"]
    if not sort: sort = "last_modified"
    models = []
    try:
        model_infos = api.list_models(author=author, pipeline_tag="text-to-image",
                                       tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit * 5)
    except Exception as e:
        print(f"Error: Failed to list models.")
        print(e)
        return models
    for model in model_infos:
        if not model.private and not model.gated:
           if not_tag and not_tag in model.tags: continue
           models.append(model.id)
           if len(models) == limit: break
    return models


def get_t2i_model_info_dict(repo_id: str):
    from huggingface_hub import HfApi
    api = HfApi()
    info = {"md": "None"}
    try:
        if not is_repo_name(repo_id) or not api.repo_exists(repo_id=repo_id): return info
        model = api.model_info(repo_id=repo_id)
    except Exception as e:
        print(f"Error: Failed to get {repo_id}'s info.")
        print(e)
        return info
    if model.private or model.gated: return info
    try:
        tags = model.tags
    except Exception as e:
        print(e)
        return info
    if not 'diffusers' in model.tags: return info
    if 'diffusers:StableDiffusionXLPipeline' in tags: info["ver"] = "SDXL"
    elif 'diffusers:StableDiffusionPipeline' in tags: info["ver"] = "SD1.5"
    elif 'diffusers:StableDiffusion3Pipeline' in tags: info["ver"] = "SD3"
    else: info["ver"] = "Other"
    info["url"] = f"https://huggingface.co/{repo_id}/"
    info["tags"] = model.card_data.tags if model.card_data and model.card_data.tags else []
    info["downloads"] = model.downloads
    info["likes"] = model.likes
    info["last_modified"] = model.last_modified.strftime("lastmod: %Y-%m-%d")
    un_tags = ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']
    descs = [info["ver"]] + list_sub(info["tags"], un_tags) + [f'DLs: {info["downloads"]}'] + [f'❤: {info["likes"]}'] + [info["last_modified"]]
    info["md"] = f'Model Info: {", ".join(descs)} [Model Repo]({info["url"]})'
    return info


def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
    from datetime import datetime, timezone, timedelta
    progress(0, desc="Updating gallery...")
    dt_now = datetime.now(timezone(timedelta(hours=9)))
    basename = dt_now.strftime('%Y%m%d_%H%M%S_')
    i = 1
    if not images: return images
    output_images = []
    output_paths = []
    for image in images:
        filename = f'{image[1]}_{basename}{str(i)}.png'
        i += 1
        oldpath = Path(image[0])
        newpath = oldpath
        try:
            if oldpath.stem == "image" and oldpath.exists():
                newpath = oldpath.resolve().rename(Path(filename).resolve())
        except Exception as e:
           print(e)
           pass
        finally:
            output_paths.append(str(newpath))
            output_images.append((str(newpath), str(filename)))
    progress(1, desc="Gallery updated.")
    return gr.update(value=output_images), gr.update(value=output_paths)


# https://github.com/gradio-app/gradio/blob/main/gradio/external.py
# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
def load_from_model(model_name: str, hf_token: str = None):
    import httpx
    import huggingface_hub
    from gradio.exceptions import ModelNotFoundError
    model_url = f"https://huggingface.co/{model_name}"
    api_url = f"https://api-inference.huggingface.co/models/{model_name}"
    print(f"Fetching model from: {model_url}")

    headers = {"Authorization": f"Bearer {hf_token}"} if hf_token is not None else {}
    response = httpx.request("GET", api_url, headers=headers)
    if response.status_code != 200:
        raise ModelNotFoundError(
            f"Could not find model: {model_name}. If it is a private or gated model, please provide your Hugging Face access token (https://huggingface.co/settings/tokens) as the argument for the `hf_token` parameter."
        )
    headers["X-Wait-For-Model"] = "true"
    client = huggingface_hub.InferenceClient(model=model_name, headers=headers, token=hf_token)
    inputs = gr.components.Textbox(label="Input")
    outputs = gr.components.Image(label="Output")
    fn = client.text_to_image

    def query_huggingface_inference_endpoints(*data):
        return fn(*data)

    interface_info = {
        "fn": query_huggingface_inference_endpoints,
        "inputs": inputs,
        "outputs": outputs,
        "title": model_name,
    }
    return gr.Interface(**interface_info)


def load_model(model_name: str):
    global loaded_models
    global model_info_dict
    if model_name in loaded_models.keys(): return loaded_models[model_name]
    try:
        loaded_models[model_name] = load_from_model(model_name)
        print(f"Loaded: {model_name}")
    except Exception as e:
        if model_name in loaded_models.keys(): del loaded_models[model_name]
        print(f"Failed to load: {model_name}")
        print(e)
        return None
    try:
        model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
        print(f"Assigned: {model_name}")
    except Exception as e:
        if model_name in model_info_dict.keys(): del model_info_dict[model_name]
        print(f"Failed to assigned: {model_name}")
        print(e)
    return loaded_models[model_name]


async def async_load_models(models: list, limit: int=5):
    sem = asyncio.Semaphore(limit)
    async def async_load_model(model: str):
        async with sem:
           try:
               await asyncio.sleep(0.5)
               return await asyncio.to_thread(load_model, model)
           except Exception as e:
               print(e)
    tasks = [asyncio.create_task(async_load_model(model)) for model in models]
    return await asyncio.gather(*tasks, return_exceptions=True)


def load_models(models: list, limit: int=5):
    loop = asyncio.new_event_loop()
    try:
        loop.run_until_complete(async_load_models(models, limit))
    except Exception as e:
        print(e)
        pass
    finally:
        loop.close()


positive_prefix = {
    "Pony": to_list("score_9, score_8_up, score_7_up"),
    "Pony Anime": to_list("source_anime, anime, score_9, score_8_up, score_7_up"),
}
positive_suffix = {
    "Common": to_list("highly detailed, masterpiece, best quality, very aesthetic, absurdres"),
    "Anime": to_list("anime artwork, anime style, studio anime, highly detailed"),
}
negative_prefix = {
    "Pony": to_list("score_6, score_5, score_4"),
    "Pony Anime": to_list("score_6, score_5, score_4, source_pony, source_furry, source_cartoon"),
    "Pony Real": to_list("score_6, score_5, score_4, source_anime, source_pony, source_furry, source_cartoon"),
}
negative_suffix = {
    "Common": to_list("lowres, (bad), bad hands, bad feet, text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]"),
    "Pony Anime": to_list("busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends"),
    "Pony Real": to_list("ugly, airbrushed, simple background, cgi, cartoon, anime"),
}
positive_all = negative_all = []
for k, v in (positive_prefix | positive_suffix).items():
    positive_all = positive_all + v + [s.replace("_", " ") for s in v]
positive_all = list_uniq(positive_all)
for k, v in (negative_prefix | negative_suffix).items():
    negative_all = negative_all + v + [s.replace("_", " ") for s in v]
positive_all = list_uniq(positive_all)


def recom_prompt(prompt: str = "", neg_prompt: str = "", pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = []):
    def flatten(src):
        return [item for row in src for item in row]
    prompts = to_list(prompt)
    neg_prompts = to_list(neg_prompt)
    prompts = list_sub(prompts, positive_all)
    neg_prompts = list_sub(neg_prompts, negative_all)
    last_empty_p = [""] if not prompts and type != "None" else []
    last_empty_np = [""] if not neg_prompts and type != "None" else []
    prefix_ps = flatten([positive_prefix.get(s, []) for s in pos_pre])
    suffix_ps = flatten([positive_suffix.get(s, []) for s in pos_suf])
    prefix_nps = flatten([negative_prefix.get(s, []) for s in neg_pre])
    suffix_nps = flatten([negative_suffix.get(s, []) for s in neg_suf])
    prompt = ", ".join(list_uniq(prefix_ps + prompts + suffix_ps) + last_empty_p)
    neg_prompt = ", ".join(list_uniq(prefix_nps + neg_prompts + suffix_nps) + last_empty_np)
    return prompt, neg_prompt


recom_prompt_type = {
    "None": ([], [], [], []),
    "Auto": ([], [], [], []),
    "Common": ([], ["Common"], [], ["Common"]),
    "Animagine": ([], ["Common", "Anime"], [], ["Common"]),
    "Pony": (["Pony"], ["Common"], ["Pony"], ["Common"]),
    "Pony Anime": (["Pony", "Pony Anime"], ["Common", "Anime"], ["Pony", "Pony Anime"], ["Common", "Pony Anime"]),
    "Pony Real": (["Pony"], ["Common"], ["Pony", "Pony Real"], ["Common", "Pony Real"]),
}


enable_auto_recom_prompt = False
def insert_recom_prompt(prompt: str = "", neg_prompt: str = "", type: str = "None"):
    global enable_auto_recom_prompt
    if type == "Auto":  enable_auto_recom_prompt = True
    else: enable_auto_recom_prompt = False
    pos_pre, pos_suf, neg_pre, neg_suf = recom_prompt_type.get(type, ([], [], [], []))
    return recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)


def set_recom_prompt_preset(type: str = "None"):
    pos_pre, pos_suf, neg_pre, neg_suf = recom_prompt_type.get(type, ([], [], [], []))
    return pos_pre, pos_suf, neg_pre, neg_suf


def get_recom_prompt_type():
    type = list(recom_prompt_type.keys())
    type.remove("Auto")
    return type


def get_positive_prefix():
    return list(positive_prefix.keys())


def get_positive_suffix():
    return list(positive_suffix.keys())


def get_negative_prefix():
    return list(negative_prefix.keys())


def get_negative_suffix():
    return list(negative_suffix.keys())


def get_tag_type(pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = []):
    tag_type = "danbooru"
    words = pos_pre + pos_suf + neg_pre + neg_suf
    for word in words:
        if "Pony" in word:
            tag_type = "e621"
            break
    return tag_type


def get_model_info_md(model_name: str):
    if model_name in model_info_dict.keys(): return model_info_dict[model_name].get("md", "")


def change_model(model_name: str):
    load_model(model_name)
    return get_model_info_md(model_name)


def infer(prompt: str, neg_prompt: str, model_name: str):
    from PIL import Image
    import random
    seed = ""
    rand = random.randint(1, 500)
    for i in range(rand):
        seed += " "
    caption = model_name.split("/")[-1]
    try:
        model = load_model(model_name)
        if not model: return (Image.Image(), None)
        image_path = model(prompt + seed, neg_prompt)
        image = Image.open(image_path).convert('RGBA')
    except Exception as e:
        print(e)
        return (Image.Image(), None)
    return (image, caption)


async def infer_multi(prompt: str, neg_prompt: str, results: list, image_num: float, model_name: str,
                 pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], progress=gr.Progress(track_tqdm=True)):
    import asyncio
    progress(0, desc="Start inference.")
    image_num = int(image_num)
    images = results if results else []
    image_num_offset = len(images)
    prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
    tasks = [asyncio.to_thread(infer, prompt, neg_prompt, model_name) for i in range(image_num)]
    for task in tasks:
        progress(float(len(images) - image_num_offset) / float(image_num), desc="Running inference.")
        try:
            result = await task
        except Exception as e:
            print(e)
            task.cancel()
            result = None
            image_num_offset += 1
        with lock:
            if result and len(result) == 2 and result[1]: images.append(result)
        await asyncio.sleep(0.05)
        yield images


async def infer_multi_random(prompt: str, neg_prompt: str, results: list, image_num: float, 
                 pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], progress=gr.Progress(track_tqdm=True)):
    import random
    progress(0, desc="Start inference.")
    image_num = int(image_num)
    images = results if results else []
    image_num_offset = len(images)
    random.seed()
    model_names = random.choices(list(loaded_models.keys()), k = image_num)
    prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
    tasks = [asyncio.to_thread(infer, prompt, neg_prompt, model_name) for model_name in model_names]
    for task in tasks:
        progress(float(len(images) - image_num_offset) / float(image_num), desc="Running inference.")
        try:
            result = await task
        except Exception as e:
            print(e)
            task.cancel()
            result = None
            image_num_offset += 1
        with lock:
            if result and len(result) == 2 and result[1]: images.append(result)
        await asyncio.sleep(0.05)
        yield images