import argparse
from pathlib import Path

import gradio as gr
import torch
from diffusers import DiffusionPipeline

from visual_anagrams.views import get_views, VIEW_MAP_NAMES
from visual_anagrams.samplers import sample_stage_1, sample_stage_2
from visual_anagrams.utils import add_args, save_illusion, save_metadata

stage_1 = DiffusionPipeline.from_pretrained(
                "DeepFloyd/IF-I-M-v1.0",
                variant="fp16",
                torch_dtype=torch.float16)
stage_2 = DiffusionPipeline.from_pretrained(
                "DeepFloyd/IF-II-M-v1.0",
                text_encoder=None,
                variant="fp16",
                torch_dtype=torch.float16,
            )
stage_1.enable_model_cpu_offload()
stage_2.enable_model_cpu_offload()


def generate_content(
    style,
    prompt_for_original,
    prompt_for_transformed,
    transformation,
    num_inference_steps,
    seed
):
    prompts = [prompt_for_original, prompt_for_transformed]
    prompt_embeds = [stage_1.encode_prompt(f'{style} {p}'.strip()) for p in [prompts]]
    prompt_embeds, negative_prompt_embeds = zip(*prompt_embeds)
    prompt_embeds = torch.cat(prompt_embeds)
    negative_prompt_embeds = torch.cat(negative_prompt_embeds)

    views = ['identity', transformation]
    views = get_views(views)

    generator = torch.manual_seed(seed)
    image = sample_stage_1(stage_1,
                            prompt_embeds,
                            negative_prompt_embeds,
                            views,
                            num_inference_steps=num_inference_steps,
                            generator=generator)

    image = sample_stage_2(stage_2,
                           image,
                           prompt_embeds,
                           negative_prompt_embeds,
                           views,
                           num_inference_steps=num_inference_steps,
                           generator=generator)
    
    return image, image_transformed, transformation_gif


choices = list(VIEW_MAP_NAMES.keys())
gradio_app = gr.Interface(
    fn=generate_content,
    inputs=[
        gr.Textbox(label="Style", placeholder="an oil painting of"),
        gr.Textbox(label="Prompt for original view", placeholder="a penguin"),
        gr.Textbox(label="Prompt for transformed view", placeholder="a giraffe"),
        gr.Dropdown(label="View transformation", choices=choices, value=choices[0]),
        gr.Number(label="Number of diffusion steps", value=30, step=1, minimum=1, maximum=100),
        gr.Number(label="Random seed", value=0, step=1, minimum=0, maximum=100000)
    ],
    outputs=[gr.Image(label="Illusion"), gr.Image(label="Original"), gr.Image(label="Transformed")],
)


if __name__ == "__main__":
    gradio_app.launch(server_name="0.0.0.0") # server_name="0.0.0.0"