#import spaces
import gradio as gr
import numpy as np
import random
import python
import torch
import os
from huggingface_hub import hf_hub_download
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from peft import PeftModel

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
token = os.getenv("HF_TKN")

# good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype, token=token).to(device)
# pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, token=token).to(device)
torch.cuda.empty_cache()

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048  # not used anymore

# Bind the custom method
# pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
# python.model_loading()



def infer(prompt, seed=42, randomize_seed=True, aspect_ratio="4:3 landscape 1152x896", lora_weight="lora_weight_rank_32_alpha_32.safetensors",
          guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):

    # Randomize seed if requested
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    
    # Load the selected LoRA weight and fuse it
    lora_weight_path = os.path.join("loras", lora_weight)
    # pipe.load_lora_weights(weight_path)
    # pipe.fuse_lora()
    torch.cuda.empty_cache()
    image, seed = python.generate_image(
        prompt,
        guidance_scale,
        aspect_ratio,
        seed,
        num_inference_steps,
        lora_weight,
    )
    # Generate images
    # for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
    #         prompt=prompt,
    #         guidance_scale=guidance_scale,
    #         num_inference_steps=num_inference_steps,
    #         width=width,
    #         height=height,
    #         generator=generator,
    #         output_type="pil",
    #         good_vae=good_vae,
    #     ):
    #     out_img = img
    return image,seed

# Examples for the prompt
examples = [
    "Photo on a small glass panel. Color. A vintage Autochrome photograph, early 1900s aesthetic depicts four roses in a brown vase with dark background.",
    "Photo on a small glass panel. Color. A depiction of trees with orange leaves and a small path.",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# Text2Autochrome demo! 
        """)
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=5,
                placeholder="Enter your prompt",
                container=False,
            )
            run_button = gr.Button("Run", scale=0)
        
        result = gr.Image(label="Result", show_label=False)
        
        with gr.Accordion("Advanced Settings", open=True):
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            # Dropdown for aspect ratio selection
            aspect_ratio = gr.Dropdown(
                label="Aspect Ratio",
                choices=["1:1 square 1024x1024", "3:4 portrait 896x1152", "5:8 portrait 832x1216", "9:16 portrait 768x1344", "4:3 landscape 1152x896", "3:2 landscape 1216x832", "16:9 landscape 1344x768"],
                value="4:3 landscape 1152x896",
                interactive=True,
            )
            
            # Dropdown for LoRA weight selection
            lora_weight = gr.Dropdown(
                label="LoRA Weight",
                choices=[
                    "lora_weight_rank_16_alpha_32_1.safetensors",
                    "lora_weight_rank_16_alpha_32_2.safetensors",
                    "lora_weight_rank_32_alpha_32.safetensors",
                    "lora_weight_rank_32_alpha_64.safetensors",
                ],
                value="lora_weight_rank_16_alpha_32_1.safetensors",
                interactive=True,
            )
            
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=25,
                    step=0.1,
                    value=8.5,
                )
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=50,
                )
        
        gr.Examples(
            examples=examples,
            fn=infer,
            inputs=[prompt],
            outputs=[result, seed],
            cache_examples=False
        )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[prompt, seed, randomize_seed, aspect_ratio, lora_weight, guidance_scale, num_inference_steps],
        outputs=[result, seed]
    )

demo.launch()