#[cfg(feature = "mkl")]
extern crate intel_mkl_src;

#[cfg(feature = "accelerate")]
extern crate accelerate_src;

use anyhow::Error as E;
use clap::Parser;

use candle::{DType, Device, Result, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::models::blip;

use tokenizers::Tokenizer;

#[derive(Parser)]
struct Args {
    #[arg(long)]
    model: Option<String>,

    #[arg(long)]
    tokenizer: Option<String>,

    #[arg(long)]
    image: String,

    /// Run on CPU rather than on GPU.
    #[arg(long)]
    cpu: bool,
}

const SEP_TOKEN_ID: u32 = 102;

/// Loads an image from disk using the image crate, this returns a tensor with shape
/// (3, 384, 384). OpenAI normalization is applied.
pub fn load_image<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
    let img = image::io::Reader::open(p)?
        .decode()
        .map_err(candle::Error::wrap)?
        .resize_to_fill(384, 384, image::imageops::FilterType::Triangle);
    let img = img.to_rgb8();
    let data = img.into_raw();
    let data = Tensor::from_vec(data, (384, 384, 3), &Device::Cpu)?.permute((2, 0, 1))?;
    let mean =
        Tensor::new(&[0.48145466f32, 0.4578275, 0.40821073], &Device::Cpu)?.reshape((3, 1, 1))?;
    let std = Tensor::new(&[0.26862954f32, 0.261_302_6, 0.275_777_1], &Device::Cpu)?
        .reshape((3, 1, 1))?;
    (data.to_dtype(candle::DType::F32)? / 255.)?
        .broadcast_sub(&mean)?
        .broadcast_div(&std)
}

pub fn main() -> anyhow::Result<()> {
    let args = Args::parse();

    let device = candle_examples::device(args.cpu)?;

    let image = load_image(args.image)?.to_device(&device)?;
    println!("loaded image {image:?}");

    let model_file = match args.model {
        None => {
            let api = hf_hub::api::sync::Api::new()?;
            let api = api.repo(hf_hub::Repo::with_revision(
                "Salesforce/blip-image-captioning-large".to_string(),
                hf_hub::RepoType::Model,
                "refs/pr/18".to_string(),
            ));
            api.get("model.safetensors")?
        }
        Some(model) => model.into(),
    };
    let tokenizer = match args.tokenizer {
        None => {
            let api = hf_hub::api::sync::Api::new()?;
            let api = api.model("Salesforce/blip-image-captioning-large".to_string());
            api.get("tokenizer.json")?
        }
        Some(file) => file.into(),
    };
    let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
    let mut tokenizer = TokenOutputStream::new(tokenizer);
    let mut logits_processor =
        candle_transformers::generation::LogitsProcessor::new(1337, None, None);

    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
    let config = blip::Config::image_captioning_large();
    let model = blip::BlipForConditionalGeneration::new(&config, vb)?;
    let vision_model = model.vision_model();
    let text_decoder = model.text_decoder();
    println!("model built");
    // TODO: Maybe add support for the conditional prompt.
    let image_embeds = image.unsqueeze(0)?.apply(vision_model)?;

    let mut token_ids = vec![30522u32];
    for _index in 0..1000 {
        let input_ids = Tensor::new(token_ids.as_slice(), &device)?.broadcast_left(1)?;
        let logits = text_decoder.forward(&input_ids, &image_embeds)?;
        let logits = logits.squeeze(0)?;
        let logits = logits.get(logits.dim(0)? - 1)?;
        let token = logits_processor.sample(&logits)?;
        if token == SEP_TOKEN_ID {
            break;
        }
        token_ids.push(token);
        if let Some(t) = tokenizer.next_token(token)? {
            use std::io::Write;
            print!("{t}");
            std::io::stdout().flush()?;
        }
    }
    if let Some(rest) = tokenizer.decode_rest().map_err(E::msg)? {
        print!("{rest}");
    }

    Ok(())
}