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

#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::t5;

use anyhow::{anyhow, Error as E, Result};
use candle::{DType, Tensor};
use candle_nn::VarBuilder;
use clap::Parser;
use hf_hub::{api::sync::Api, Cache, Repo, RepoType};
use tokenizers::Tokenizer;

const DTYPE: DType = DType::F32;
const DEFAULT_PROMPT: &str = "Translate English to German: That is good.";

#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
    /// Run on CPU rather than on GPU.
    #[arg(long)]
    cpu: bool,

    /// Run offline (you must have the files already cached)
    #[arg(long)]
    offline: bool,

    /// Enable tracing (generates a trace-timestamp.json file).
    #[arg(long)]
    tracing: bool,

    /// The model repository to use on the HuggingFace hub.
    #[arg(long)]
    model_id: Option<String>,

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

    /// Compute embeddings for this prompt or use the DEFAULT_PROMPT.
    #[arg(long)]
    prompt: Option<String>,

    /// The number of times to run the prompt.
    #[arg(long, default_value = "1")]
    n: usize,
}

impl Args {
    fn build_model_and_tokenizer(&self) -> Result<(t5::T5EncoderModel, Tokenizer)> {
        let device = candle_examples::device(self.cpu)?;
        let default_model = "t5-small".to_string();
        let default_revision = "refs/pr/15".to_string();
        let (model_id, revision) = match (self.model_id.to_owned(), self.revision.to_owned()) {
            (Some(model_id), Some(revision)) => (model_id, revision),
            (Some(model_id), None) => (model_id, "main".to_string()),
            (None, Some(revision)) => (default_model, revision),
            (None, None) => (default_model, default_revision),
        };

        let repo = Repo::with_revision(model_id, RepoType::Model, revision);
        let (config_filename, tokenizer_filename, weights_filename) = if self.offline {
            let cache = Cache::default().repo(repo);
            (
                cache
                    .get("config.json")
                    .ok_or(anyhow!("Missing config file in cache"))?,
                cache
                    .get("tokenizer.json")
                    .ok_or(anyhow!("Missing tokenizer file in cache"))?,
                cache
                    .get("model.safetensors")
                    .ok_or(anyhow!("Missing weights file in cache"))?,
            )
        } else {
            let api = Api::new()?;
            let api = api.repo(repo);
            (
                api.get("config.json")?,
                api.get("tokenizer.json")?,
                api.get("model.safetensors")?,
            )
        };
        let config = std::fs::read_to_string(config_filename)?;
        let config: t5::Config = serde_json::from_str(&config)?;
        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;

        let weights = unsafe { candle::safetensors::MmapedFile::new(weights_filename)? };
        let weights = weights.deserialize()?;
        let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, &device);
        let model = t5::T5EncoderModel::load(vb, &config)?;
        Ok((model, tokenizer))
    }
}

fn main() -> Result<()> {
    let args = Args::parse();
    let start = std::time::Instant::now();

    let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
    let prompt = args.prompt.unwrap_or_else(|| DEFAULT_PROMPT.to_string());
    let tokenizer = tokenizer
        .with_padding(None)
        .with_truncation(None)
        .map_err(E::msg)?;
    let tokens = tokenizer
        .encode(prompt, true)
        .map_err(E::msg)?
        .get_ids()
        .to_vec();
    let token_ids = Tensor::new(&tokens[..], model.device())?.unsqueeze(0)?;
    println!("Loaded and encoded {:?}", start.elapsed());
    for idx in 0..args.n {
        let start = std::time::Instant::now();
        let ys = model.forward(&token_ids)?;
        if idx == 0 {
            println!("{ys}");
        }
        println!("Took {:?}", start.elapsed());
    }
    Ok(())
}