import os
import subprocess
import logging
import re
import random
import string
import requests
import sys
import warnings
    
import spaces
import torch
import gradio as gr
import numpy as np
from lxml.html import fromstring
#from transformers import pipeline
from torch import multiprocessing as mp, nn
#from torch.multiprocessing import Pool
#from pathos.multiprocessing import ProcessPool as Pool
#from pathos.threading import ThreadPool as Pool
#from diffusers.pipelines.flux import FluxPipeline
from diffusers.utils import export_to_gif, load_image
from diffusers.models.modeling_utils import ModelMixin
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file, save_file
from diffusers import DiffusionPipeline, AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler, DDIMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel, AutoencoderKL, UNet3DConditionModel
#import jax
#import jax.numpy as jnp
from numba import jit,njit

warnings.filterwarnings("ignore")
root = logging.getLogger()
root.setLevel(logging.DEBUG)
handler = logging.StreamHandler(sys.stdout)
handler.setLevel(logging.DEBUG)
formatter = logging.Formatter('\n >>> [%(levelname)s] %(asctime)s %(name)s: %(message)s\n')
handler.setFormatter(formatter)
root.addHandler(handler)
handler2 = logging.StreamHandler(sys.stderr)
handler2.setLevel(logging.DEBUG)
formatter = logging.Formatter('\n >>> [%(levelname)s] %(asctime)s %(name)s: %(message)s\n')
handler2.setFormatter(formatter)
root.addHandler(handler2)

last_motion=None
dtype = torch.float16
result=[]
device = "cuda"
#repo = "ByteDance/AnimateDiff-Lightning"
#ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
base = "emilianJR/epiCRealism"
#base = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(device, dtype=dtype)
#unet = UNet2DConditionModel.from_config("emilianJR/epiCRealism",subfolder="unet").to(device, dtype).load_state_dict(load_file(hf_hub_download("emilianJR/epiCRealism", "unet/diffusion_pytorch_model.safetensors"), device=device), strict=False)
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3", torch_dtype=dtype, device=device)

fast=True
fps=10
time=1
width=384
height=768
step=40
accu=10

css="""
input, input::placeholder {
    text-align: center !important;
}
*, *::placeholder {
    font-family: Suez One !important;
}
h1,h2,h3,h4,h5,h6 {
    width: 100%;
    text-align: center;
}
footer {
    display: none !important;
}
#col-container {
    margin: 0 auto;
    max-width: 15cm;
}
.image-container {
    aspect-ratio: """+str(width)+"/"+str(height)+""" !important;
}
.dropdown-arrow {
    display: none !important;
}
*:has(>.btn) {
    display: flex;
    justify-content: space-evenly;
    align-items: center;
}
.btn {
    display: flex;
}
"""

js="""
function custom(){
    document.querySelector("div#prompt input").setAttribute("maxlength","38")
    document.querySelector("div#prompt2 input").setAttribute("maxlength","38")
}
"""

@njit(cache=True,nopython=True,parallel=True)
def run(cmd, assert_success=False, capture_output=False, env=None, dry_run=False):
    if dry_run:
        print(f"--> {cmd}")
        result = 1
    else:
        result = subprocess.run(cmd, shell=True, capture_output=capture_output, env=env)
        if assert_success and result.returncode != 0:
            logging.error(
                f"Command '{cmd}' failed with exit status code '{result.returncode}'. Exiting..."
            )
            sys.exit()

    return result

@njit(cache=True,nopython=True,parallel=True)
def translate(text,lang):
    if text == None or lang == None:
        return ""       
    text = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', text)).lower().strip()
    lang = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', lang)).lower().strip()    
    if text == "" or lang == "":
        return ""
    if len(text) > 38:
        raise Exception("Translation Error: Too long text!")
    user_agents = [
        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 13_1) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15'
    ]
    padded_chars = re.sub("[(^\-)(\-$)]","",text.replace("","-").replace("- -"," ")).strip()
    query_text = f'Please translate {padded_chars}, into {lang}'
    url = f'https://www.google.com/search?q={query_text}'
    resp = requests.get(
        url = url,
        headers = {
            'User-Agent': random.choice(user_agents)
        }
    )
    content = resp.content
    html = fromstring(content)
    translated = text
    try:
        src_lang = html.xpath('//*[@class="source-language"]')[0].text_content().lower().strip()
        trgt_lang = html.xpath('//*[@class="target-language"]')[0].text_content().lower().strip()
        src_text = html.xpath('//*[@id="tw-source-text"]/*')[0].text_content().lower().strip()
        trgt_text = html.xpath('//*[@id="tw-target-text"]/*')[0].text_content().lower().strip()
        if trgt_lang == lang:
            translated = trgt_text
    except:
        print(f'Translation Warning: Failed To Translate!')
    ret = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', translated)).lower().strip()
    print(ret)
    return ret

@njit(cache=True,nopython=True,parallel=True)
def generate_random_string(length):
    characters = string.ascii_letters + string.digits
    return ''.join(random.choice(characters) for _ in range(length))

#@spaces.GPU(duration=65)
@njit(cache=True,nopython=True,parallel=True)
def Piper(image,positive,negative,motion):
    global last_motion
    global ip_loaded

    if last_motion != motion:
        pipe.unload_lora_weights()
        if motion != "":
            pipe.load_lora_weights(motion, adapter_name="motion")
            pipe.fuse_lora()
            pipe.set_adapters(["motion"], [0.7])
        last_motion = motion

    pipe.to(device,dtype)

    if negative=="":
        return pipe(
            prompt=positive,
            height=height,
            width=width,
            ip_adapter_image=image.convert("RGB").resize((width,height)),
            num_inference_steps=step,
            guidance_scale=accu,
            num_frames=(fps*time)
        )
    
    return pipe(
        prompt=positive,
        negative_prompt=negative,
        height=height,
        width=width,
        ip_adapter_image=image.convert("RGB").resize((width,height)),
        num_inference_steps=step,
        guidance_scale=accu,
        num_frames=(fps*time)
    )

@njit(cache=True,nopython=True,parallel=True)
def infer(pm):
        print("infer: started")
    
        p1 = pm["p"]
        name = generate_random_string(12)+".png"
    
        neg = pm["n"]
        if neg != "":
            neg = f"{neg} where in the image"

        _do = ['photographed', 'realistic', 'dynamic poze', 'deep field', 'reasonable', "natural", 'rough', 'best quality', 'focused', "highly detailed"]
        if p1 != "":
            _do.append(f"a new {p1} content in the image")
        posi = ", ".join(_do)

        if pm["i"] == None:
            return None
        out = Piper(pm["i"],posi,neg,pm["m"])
        export_to_gif(out.frames[0],name,fps=fps)
        return name

@njit(cache=True,nopython=True,parallel=True)
def handle(i,m,p1,p2,*result):    
    p1_en = translate(p1,"english")
    p2_en = translate(p2,"english")
    pm = {"p":p1_en,"n":p2_en,"m":m,"i":i}
    ln = len(result)
    rng = list(range(ln))
    arr = [pm for _ in rng]
    #with Pool(f'{ ln }:ppn=2', queue='productionQ', timelimit='5:00:00', workdir='.') as pool:
        #return pool.map(infer,arr)
    ret = []
    for _ in range(ln):
        ret.append(infer,pm)
    return ret

@njit(cache=True,nopython=True,parallel=True)
def ui():
    with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo:
        with gr.Column(elem_id="col-container"):
            gr.Markdown(f"""
                # MULTI-LANGUAGE IMAGE GENERATOR
            """)
            with gr.Row():
                img = gr.Image(label="STATIC PHOTO",show_label=True,container=True,type="pil")
            with gr.Row():
                prompt = gr.Textbox(
                    elem_id="prompt",
                    placeholder="INCLUDE",
                    container=False,
                    max_lines=1
                )
            with gr.Row():
                prompt2 = gr.Textbox(
                    elem_id="prompt2",
                    placeholder="EXCLUDE",
                    container=False,
                    max_lines=1
                )
            with gr.Row():
                    motion = gr.Dropdown(
                        label='CAMERA',
                        show_label=True,
                        container=True,
                        choices=[
                            ("(No Effect)", ""),
                            ("Zoom in", "guoyww/animatediff-motion-lora-zoom-in"),
                            ("Zoom out", "guoyww/animatediff-motion-lora-zoom-out"),
                            ("Tilt up", "guoyww/animatediff-motion-lora-tilt-up"),
                            ("Tilt down", "guoyww/animatediff-motion-lora-tilt-down"),
                            ("Pan left", "guoyww/animatediff-motion-lora-pan-left"),
                            ("Pan right", "guoyww/animatediff-motion-lora-pan-right"),
                            ("Roll left", "guoyww/animatediff-motion-lora-rolling-anticlockwise"),
                            ("Roll right", "guoyww/animatediff-motion-lora-rolling-clockwise"),
                        ],
                        value="",
                        interactive=True
                    )
            with gr.Row():
                    run_button = gr.Button("START",elem_classes="btn",scale=0)
            with gr.Row():
                    result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
                    result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
                
        gr.on(
            triggers=[run_button.click, prompt.submit, prompt2.submit],
            fn=handle,inputs=[img,motion,prompt,prompt2,*result],outputs=result
        )
        demo.queue().launch()

@njit(cache=True,nopython=True,parallel=True)
def pre():
    global pipe

    pipe = AnimateDiffPipeline.from_pretrained(base, vae=vae, motion_adapter=adapter, torch_dtype=dtype).to(device)
    pipe.scheduler = DDIMScheduler(
        clip_sample=False,
        beta_start=0.00085,
        beta_end=0.012,
        beta_schedule="linear",
        timestep_spacing="trailing",
        steps_offset=1
    )
    pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
    pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
    pipe.enable_vae_slicing()
    pipe.enable_free_init(method="butterworth", use_fast_sampling=fast)

    mp.set_start_method("spawn", force=True)

pre()
ui()