# built-in

from inspect import signature
import os
import subprocess
import logging
import re
import random
from string import ascii_letters, digits, punctuation
import requests
import sys
import warnings
import time
import asyncio
from functools import partial

# external

import spaces
import torch
import gradio as gr
from pathos.multiprocessing import ProcessPool as Pool
from numpy import asarray as array
from lxml.html import fromstring
from diffusers.utils import export_to_gif, load_image
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file, save_file
from diffusers import FluxPipeline, DiffusionPipeline, AnimateDiffPipeline, MotionAdapter, EulerAncestralDiscreteScheduler, DDIMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel, AutoencoderKL, UNet3DConditionModel

# logging

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)

# constant data

dtype = torch.float16
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)

# variable data

last_motion=""
result = []

# precision data

seq=512
fast=False
fps=10
time=1
width=896
height=896
step=50
accu=7.5

# ui data

css="".join(["""
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")
}
"""

# torch pipes

pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter).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-plus_sd15.bin")
pipe.enable_free_init(method="butterworth", use_fast_sampling=fast)

pipe_flux = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, token=os.getenv("hf_token")).to(device,dtype=dtype)

# Parallelism

def parallel(func,*args):
    with Pool(nodes=len(args)) as pool:
        res = pool.imap(func, *args)
    return list(res)
        
# functionality
        
def run(cmd):
    return str(subprocess.run(cmd, shell=True, capture_output=True, env=None).stdout)

def xpath_finder(str,pattern):
    try:
        return ""+fromstring(str).xpath(pattern)[0].text_content().lower().strip()
    except:
        return ""

def translate(text,lang):
    if text == None or lang == None:
        return ""       
    text = re.sub(f'[{punctuation}]', '', re.sub('[\s+]', ' ', text)).lower().strip()
    lang = re.sub(f'[{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}'
    content = str(requests.get(
        url = url,
        headers = {
            'User-Agent': random.choice(user_agents)
        }
    ).content)
    translated = text
    src_lang = xpath_finder(content,'//*[@class="source-language"]')
    trgt_lang = xpath_finder(content,'//*[@class="target-language"]')
    src_text = xpath_finder(content,'//*[@id="tw-source-text"]/*')
    trgt_text = xpath_finder(content,'//*[@id="tw-target-text"]/*')
    if trgt_lang == lang:
        translated = trgt_text
    ret = re.sub(f'[{punctuation}]', '', re.sub('[\s+]', ' ', translated)).lower().strip()
    print(ret)
    return ret
    
def generate_random_string(length):
    characters = str(ascii_letters + digits)
    return ''.join(random.choice(characters) for _ in range(length))

@spaces.GPU(duration=140)
def pipe_generate(img,p1,p2,motion):
    global last_motion
    global pipe

    if last_motion != motion:
        if last_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=dtype)

    if img == None:
        img = pipe(
            prompt=p1,
            height=height,
            width=width,
            guidance_scale=accu,
            num_inference_steps=step,
            max_sequence_length=seq,
            generator=torch.Generator("cuda").manual_seed(0)
        ).images[0]

    return pipe(
        prompt=p1,
        negative_prompt=p2,
        height=height,
        width=width,
        ip_adapter_image=img.convert("RGB"),
        num_inference_steps=step,
        guidance_scale=accu,
        num_frames=(fps*time)
    )

def handle_generate(*inp):

    inp = list(inp)
    
    inp[1] = translate(inp[1],"english")
    inp[2] = translate(inp[2],"english")

    if inp[2] != "":
        inp[2] = f", {inp[2]}"

    inp[2] = f"(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck{inp[2]}"
    
    _do = ['photographed', 'realistic', 'dynamic poze', 'deep field', 'reasonable', "natural", 'rough', 'best quality', 'focused', "highly detailed"]
    if inp[1] != "":
        _do.append(f"a new {inp[1]} content in the image")
    inp[1] = ", ".join(_do)
    
    ln = len(result)
    parallel_args = [inp for i in range(ln)]
    
    pipe_out = parallel( pipe_generate, *parallel_args )
    names = []
    for i in pipe_out:
        name = generate_random_string(12)+".png"
        export_to_gif(i.frames[0],name,fps=fps)
        names.append( name )
    return names

def ui():
    global result
    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 GIF CREATOR
            """)
            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))

        gr.on(
            triggers=[
                run_button.click,
                prompt.submit,
                prompt2.submit
            ],
            fn=handle_generate,
            inputs=[img,prompt,prompt2,motion],
            outputs=result
        )
        demo.queue().launch()

# entry

if __name__ == "__main__":
    os.chdir(os.path.abspath(os.path.dirname(__file__)))
    ui()

# end