import os
import json
import copy
import time
import random
import logging
import numpy as np
from typing import Any, Dict, List, Optional, Union
from diffusers.utils import load_image
import torch
from PIL import Image
import gradio as gr

from diffusers import (
    DiffusionPipeline,
    AutoencoderTiny,
    AutoencoderKL,
    AutoPipelineForImage2Image,
    FluxPipeline,
    FlowMatchEulerDiscreteScheduler)

from huggingface_hub import (
    hf_hub_download,
    HfFileSystem,
    ModelCard,
    snapshot_download)

import spaces

#---if workspace = local or colab---

# Authenticate with Hugging Face
# from huggingface_hub import login

# Log in to Hugging Face using the provided token
# hf_token = 'hf-token-authentication'
# login(hf_token)


def calculate_shift(
    image_seq_len,
    base_seq_len: int = 256,
    max_seq_len: int = 4096,
    base_shift: float = 0.5,
    max_shift: float = 1.16,
):
    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
    b = base_shift - m * base_seq_len
    mu = image_seq_len * m + b
    return mu

def retrieve_timesteps(
    scheduler,
    num_inference_steps: Optional[int] = None,
    device: Optional[Union[str, torch.device]] = None,
    timesteps: Optional[List[int]] = None,
    sigmas: Optional[List[float]] = None,
    **kwargs,
):
    if timesteps is not None and sigmas is not None:
        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
    if timesteps is not None:
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    elif sigmas is not None:
        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps

# FLUX pipeline
@torch.inference_mode()
def flux_pipe_call_that_returns_an_iterable_of_images(
    self,
    prompt: Union[str, List[str]] = None,
    prompt_2: Optional[Union[str, List[str]]] = None,
    height: Optional[int] = None,
    width: Optional[int] = None,
    num_inference_steps: int = 28,
    timesteps: List[int] = None,
    guidance_scale: float = 3.5,
    num_images_per_prompt: Optional[int] = 1,
    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
    latents: Optional[torch.FloatTensor] = None,
    prompt_embeds: Optional[torch.FloatTensor] = None,
    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
    output_type: Optional[str] = "pil",
    return_dict: bool = True,
    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
    max_sequence_length: int = 512,
    good_vae: Optional[Any] = None,
):
    height = height or self.default_sample_size * self.vae_scale_factor
    width = width or self.default_sample_size * self.vae_scale_factor
    
    self.check_inputs(
        prompt,
        prompt_2,
        height,
        width,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        max_sequence_length=max_sequence_length,
    )

    self._guidance_scale = guidance_scale
    self._joint_attention_kwargs = joint_attention_kwargs
    self._interrupt = False

    batch_size = 1 if isinstance(prompt, str) else len(prompt)
    device = self._execution_device

    lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
    prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
        prompt=prompt,
        prompt_2=prompt_2,
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        device=device,
        num_images_per_prompt=num_images_per_prompt,
        max_sequence_length=max_sequence_length,
        lora_scale=lora_scale,
    )
    
    num_channels_latents = self.transformer.config.in_channels // 4
    latents, latent_image_ids = self.prepare_latents(
        batch_size * num_images_per_prompt,
        num_channels_latents,
        height,
        width,
        prompt_embeds.dtype,
        device,
        generator,
        latents,
    )
    
    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
    image_seq_len = latents.shape[1]
    mu = calculate_shift(
        image_seq_len,
        self.scheduler.config.base_image_seq_len,
        self.scheduler.config.max_image_seq_len,
        self.scheduler.config.base_shift,
        self.scheduler.config.max_shift,
    )
    timesteps, num_inference_steps = retrieve_timesteps(
        self.scheduler,
        num_inference_steps,
        device,
        timesteps,
        sigmas,
        mu=mu,
    )
    self._num_timesteps = len(timesteps)

    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None

    for i, t in enumerate(timesteps):
        if self.interrupt:
            continue

        timestep = t.expand(latents.shape[0]).to(latents.dtype)

        noise_pred = self.transformer(
            hidden_states=latents,
            timestep=timestep / 1000,
            guidance=guidance,
            pooled_projections=pooled_prompt_embeds,
            encoder_hidden_states=prompt_embeds,
            txt_ids=text_ids,
            img_ids=latent_image_ids,
            joint_attention_kwargs=self.joint_attention_kwargs,
            return_dict=False,
        )[0]

        latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
        latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
        image = self.vae.decode(latents_for_image, return_dict=False)[0]
        yield self.image_processor.postprocess(image, output_type=output_type)[0]
        latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
        torch.cuda.empty_cache()
        
    latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
    latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
    image = good_vae.decode(latents, return_dict=False)[0]
    self.maybe_free_model_hooks()
    torch.cuda.empty_cache()
    yield self.image_processor.postprocess(image, output_type=output_type)[0]

#------------------------------------------------------------------------------------------------------------------------------------------------------------#
loras = [
    {
        "image": "https://huggingface.co/prithivMLmods/Flux.1-Dev-Indo-Realism-LoRA/resolve/main/images/333.png",
        "title": "Indo Realism",
        "repo": "prithivMLmods/Flux.1-Dev-Indo-Realism-LoRA",
        "weights": "indo-realism.safetensors",
        "trigger_word": "indo-realism"        
    }
    #add new
]

#--------------------------------------------------Model Initialization-----------------------------------------------------------------------------------------#

dtype = torch.bfloat16
device = "cuda"
base_model = "black-forest-labs/FLUX.1-dev"

#TAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. FLUX.1 is useful for real-time previewing of the FLUX.1 generation process.#
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model,
                                                      vae=good_vae,
                                                      transformer=pipe.transformer,
                                                      text_encoder=pipe.text_encoder,
                                                      tokenizer=pipe.tokenizer,
                                                      text_encoder_2=pipe.text_encoder_2,
                                                      tokenizer_2=pipe.tokenizer_2,
                                                      torch_dtype=dtype
                                                     )

MAX_SEED = 2**32-1

pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self
    
    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")

def update_selection(evt: gr.SelectData, width, height):
    selected_lora = loras[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✅"
    if "aspect" in selected_lora:
        if selected_lora["aspect"] == "portrait":
            width = 768
            height = 1024
        elif selected_lora["aspect"] == "landscape":
            width = 1024
            height = 768
        else:
            width = 1024
            height = 1024
    return (
        updated_text,
        evt.index,
        width,
        height,
    )

@spaces.GPU(duration=100)
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    with calculateDuration("Generating image"):
        # Generate image
        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
            prompt=prompt_mash,
            num_inference_steps=steps,
            guidance_scale=cfg_scale,
            width=width,
            height=height,
            generator=generator,
            joint_attention_kwargs={"scale": lora_scale},
            output_type="pil",
            good_vae=good_vae,
        ):
            yield img

def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
    generator = torch.Generator(device="cuda").manual_seed(seed)
    pipe_i2i.to("cuda")
    image_input = load_image(image_input_path)
    final_image = pipe_i2i(
        prompt=prompt_mash,
        image=image_input,
        strength=image_strength,
        num_inference_steps=steps,
        guidance_scale=cfg_scale,
        width=width,
        height=height,
        generator=generator,
        joint_attention_kwargs={"scale": lora_scale},
        output_type="pil",
    ).images[0]
    return final_image 

@spaces.GPU()
def run_lora(image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, prompt = "", progress=gr.Progress(track_tqdm=True)):
    if selected_index is None:
        raise gr.Error("You must select a LoRA before proceeding.🧨")
    selected_lora = loras[selected_index]
    lora_path = selected_lora["repo"]
    trigger_word = selected_lora["trigger_word"]
    if(trigger_word):
        if "trigger_position" in selected_lora:
            if selected_lora["trigger_position"] == "prepend":
                prompt_mash = f"{trigger_word} {prompt}"
            else:
                prompt_mash = f"{prompt} {trigger_word}"
        else:
            prompt_mash = f"{trigger_word} {prompt}"
    else:
        prompt_mash = prompt

    with calculateDuration("Unloading LoRA"):
        pipe.unload_lora_weights()
        pipe_i2i.unload_lora_weights()
        
    #LoRA weights flow
    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
        pipe_to_use = pipe_i2i if image_input is not None else pipe
        weight_name = selected_lora.get("weights", None)
        
        pipe_to_use.load_lora_weights(
            lora_path, 
            weight_name=weight_name, 
            low_cpu_mem_usage=True
        )
            
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
            
    if(image_input is not None):
        
        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed)
        yield final_image, seed, gr.update(visible=False)
    else:
        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
    
        final_image = None
        step_counter = 0
        for image in image_generator:
            step_counter+=1
            final_image = image
            progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
            yield image, seed, gr.update(value=progress_bar, visible=True)
            
        yield final_image, seed, gr.update(value=progress_bar, visible=False)
        
def get_huggingface_safetensors(link):
  split_link = link.split("/")
  if(len(split_link) == 2):
            model_card = ModelCard.load(link)
            base_model = model_card.data.get("base_model")
            print(base_model)
      
            #Allows Both
            if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
                raise Exception("Flux LoRA Not Found!")
                
            # Only allow "black-forest-labs/FLUX.1-dev"
            #if base_model != "black-forest-labs/FLUX.1-dev":
                #raise Exception("Only FLUX.1-dev is supported, other LoRA models are not allowed!")
                
            image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
            trigger_word = model_card.data.get("instance_prompt", "")
            image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
            fs = HfFileSystem()
            try:
                list_of_files = fs.ls(link, detail=False)
                for file in list_of_files:
                    if(file.endswith(".safetensors")):
                        safetensors_name = file.split("/")[-1]
                    if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
                      image_elements = file.split("/")
                      image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
            except Exception as e:
              print(e)
              gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
              raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
            return split_link[1], link, safetensors_name, trigger_word, image_url

def check_custom_model(link):
    if(link.startswith("https://")):
        if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
    else: 
        return get_huggingface_safetensors(link)

def add_custom_lora(custom_lora):
    global loras
    if(custom_lora):
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            print(f"Loaded custom LoRA: {repo}")
            card = f'''
            <div class="custom_lora_card">
              <span>Loaded custom LoRA:</span>
              <div class="card_internal">
                <img src="{image}" />
                <div>
                    <h3>{title}</h3>
                    <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
                </div>
              </div>
            </div>
            '''
            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
            if(not existing_item_index):
                new_item = {
                    "image": image,
                    "title": title,
                    "repo": repo,
                    "weights": path,
                    "trigger_word": trigger_word
                }
                print(new_item)
                existing_item_index = len(loras)
                loras.append(new_item)
        
            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
        except Exception as e:
            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=False), gr.update(), "", None, ""
    else:
        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

def remove_custom_lora():
    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

run_lora.zerogpu = True

with gr.Blocks(theme="prithivMLmods/Minecraft-Theme") as app:
    title = gr.HTML(
        """<h1>Arcane🥳</h1>""",
        elem_id="title",
    )
    selected_index = gr.State(None)
    with gr.Row():
        with gr.Column(scale=3):
            generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
            
    with gr.Row():
        with gr.Column():
            selected_info = gr.Markdown("")
            input_image = gr.Image(label="Input image", type="filepath")
            
            gallery = gr.Gallery(
                [(item["image"], item["title"]) for item in loras],
                label="LoRA DLC's",
                allow_preview=False,
                columns=3,
                elem_id="gallery",
                show_share_button=False
            )
           
        with gr.Column():
            progress_bar = gr.Markdown(elem_id="progress",visible=False)
            result = gr.Image(label="Generated Image")

    with gr.Row():
        with gr.Accordion("Advanced Settings", open=False):
            with gr.Row():
                image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
            with gr.Column():
                with gr.Row():
                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
                
                with gr.Row():
                    width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
                    height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
                
                with gr.Row():
                    randomize_seed = gr.Checkbox(True, label="Randomize seed")
                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=0.95)

    gallery.select(
        update_selection,
        inputs=[width, height],
        outputs=[selected_info, selected_index, width, height]
    )
    gr.on(
        triggers=[generate_button.click],
        fn=run_lora,
        inputs=[input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
        outputs=[result, seed, progress_bar]
    )

app.queue()
app.launch()