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

#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use std::io::Write;
use std::path::PathBuf;

use candle_transformers::models::t5;

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

const DTYPE: DType = DType::F32;

#[derive(Clone, Debug, Copy, ValueEnum)]
enum Which {
    T5Base,
    T5Small,
    T5Large,
    T5_3B,
    Mt5Base,
    Mt5Small,
    Mt5Large,
}

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

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

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

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

    /// Enable decoding.
    #[arg(long)]
    decode: bool,

    // Enable/disable decoding.
    #[arg(long, default_value = "false")]
    disable_cache: bool,

    /// Use this prompt, otherwise compute sentence similarities.
    #[arg(long)]
    prompt: Option<String>,

    /// If set along with --decode, will use this prompt to initialize the decoder.
    #[arg(long)]
    decoder_prompt: Option<String>,

    /// L2 normalization for embeddings.
    #[arg(long, default_value = "true")]
    normalize_embeddings: bool,

    /// The temperature used to generate samples.
    #[arg(long, default_value_t = 0.8)]
    temperature: f64,

    /// Nucleus sampling probability cutoff.
    #[arg(long)]
    top_p: Option<f64>,

    /// Penalty to be applied for repeating tokens, 1. means no penalty.
    #[arg(long, default_value_t = 1.1)]
    repeat_penalty: f32,

    /// The context size to consider for the repeat penalty.
    #[arg(long, default_value_t = 64)]
    repeat_last_n: usize,

    /// The model to be used.
    #[arg(long, default_value = "t5-small")]
    which: Which,
}

struct T5ModelBuilder {
    device: Device,
    config: t5::Config,
    weights_filename: Vec<PathBuf>,
}

impl T5ModelBuilder {
    pub fn load(args: &Args) -> Result<(Self, Tokenizer)> {
        let device = candle_examples::device(args.cpu)?;
        let (default_model, default_revision) = match args.which {
            Which::T5Base => ("t5-base", "main"),
            Which::T5Small => ("t5-small", "refs/pr/15"),
            Which::T5Large => ("t5-large", "main"),
            Which::T5_3B => ("t5-3b", "main"),
            Which::Mt5Base => ("google/mt5-base", "refs/pr/5"),
            Which::Mt5Small => ("google/mt5-small", "refs/pr/6"),
            Which::Mt5Large => ("google/mt5-large", "refs/pr/2"),
        };
        let default_model = default_model.to_string();
        let default_revision = default_revision.to_string();
        let (model_id, revision) = match (args.model_id.to_owned(), args.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.clone(), RepoType::Model, revision);
        let api = Api::new()?;
        let repo = api.repo(repo);
        let config_filename = match &args.config_file {
            None => repo.get("config.json")?,
            Some(f) => f.into(),
        };
        let tokenizer_filename = match &args.tokenizer_file {
            None => match args.which {
                Which::Mt5Base => api
                    .model("lmz/mt5-tokenizers".into())
                    .get("mt5-base.tokenizer.json")?,
                Which::Mt5Small => api
                    .model("lmz/mt5-tokenizers".into())
                    .get("mt5-small.tokenizer.json")?,
                Which::Mt5Large => api
                    .model("lmz/mt5-tokenizers".into())
                    .get("mt5-large.tokenizer.json")?,
                _ => repo.get("tokenizer.json")?,
            },
            Some(f) => f.into(),
        };
        let weights_filename = match &args.model_file {
            Some(f) => f.split(',').map(|v| v.into()).collect::<Vec<_>>(),
            None => {
                if model_id == "google/flan-t5-xxl" || model_id == "google/flan-ul2" {
                    candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?
                } else {
                    vec![repo.get("model.safetensors")?]
                }
            }
        };
        let config = std::fs::read_to_string(config_filename)?;
        let mut config: t5::Config = serde_json::from_str(&config)?;
        config.use_cache = !args.disable_cache;
        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
        Ok((
            Self {
                device,
                config,
                weights_filename,
            },
            tokenizer,
        ))
    }

    pub fn build_encoder(&self) -> Result<t5::T5EncoderModel> {
        let vb = unsafe {
            VarBuilder::from_mmaped_safetensors(&self.weights_filename, DTYPE, &self.device)?
        };
        Ok(t5::T5EncoderModel::load(vb, &self.config)?)
    }

    pub fn build_conditional_generation(&self) -> Result<t5::T5ForConditionalGeneration> {
        let vb = unsafe {
            VarBuilder::from_mmaped_safetensors(&self.weights_filename, DTYPE, &self.device)?
        };
        Ok(t5::T5ForConditionalGeneration::load(vb, &self.config)?)
    }
}

fn main() -> Result<()> {
    use tracing_chrome::ChromeLayerBuilder;
    use tracing_subscriber::prelude::*;

    let args = Args::parse();

    let _guard = if args.tracing {
        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
        tracing_subscriber::registry().with(chrome_layer).init();
        Some(guard)
    } else {
        None
    };

    let (builder, mut tokenizer) = T5ModelBuilder::load(&args)?;
    let device = &builder.device;
    let tokenizer = tokenizer
        .with_padding(None)
        .with_truncation(None)
        .map_err(E::msg)?;
    match args.prompt {
        Some(prompt) => {
            let tokens = tokenizer
                .encode(prompt, true)
                .map_err(E::msg)?
                .get_ids()
                .to_vec();
            let input_token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
            if !args.decode {
                let mut model = builder.build_encoder()?;
                let start = std::time::Instant::now();
                let ys = model.forward(&input_token_ids)?;
                println!("{ys}");
                println!("Took {:?}", start.elapsed());
            } else {
                let mut model = builder.build_conditional_generation()?;
                let mut output_token_ids = [builder
                    .config
                    .decoder_start_token_id
                    .unwrap_or(builder.config.pad_token_id)
                    as u32]
                .to_vec();
                if let Some(decoder_prompt) = &args.decoder_prompt {
                    print!("{decoder_prompt}");
                    output_token_ids.extend(
                        tokenizer
                            .encode(decoder_prompt.to_string(), false)
                            .map_err(E::msg)?
                            .get_ids()
                            .to_vec(),
                    );
                }
                let temperature = if args.temperature <= 0. {
                    None
                } else {
                    Some(args.temperature)
                };
                let mut logits_processor = LogitsProcessor::new(299792458, temperature, args.top_p);
                let encoder_output = model.encode(&input_token_ids)?;
                let start = std::time::Instant::now();

                for index in 0.. {
                    if output_token_ids.len() > 512 {
                        break;
                    }
                    let decoder_token_ids = if index == 0 || !builder.config.use_cache {
                        Tensor::new(output_token_ids.as_slice(), device)?.unsqueeze(0)?
                    } else {
                        let last_token = *output_token_ids.last().unwrap();
                        Tensor::new(&[last_token], device)?.unsqueeze(0)?
                    };
                    let logits = model
                        .decode(&decoder_token_ids, &encoder_output)?
                        .squeeze(0)?;
                    let logits = if args.repeat_penalty == 1. {
                        logits
                    } else {
                        let start_at = output_token_ids.len().saturating_sub(args.repeat_last_n);
                        candle_transformers::utils::apply_repeat_penalty(
                            &logits,
                            args.repeat_penalty,
                            &output_token_ids[start_at..],
                        )?
                    };

                    let next_token_id = logits_processor.sample(&logits)?;
                    if next_token_id as usize == builder.config.eos_token_id {
                        break;
                    }
                    output_token_ids.push(next_token_id);
                    if let Some(text) = tokenizer.id_to_token(next_token_id) {
                        let text = text.replace('▁', " ").replace("<0x0A>", "\n");
                        print!("{text}");
                        std::io::stdout().flush()?;
                    }
                }
                let dt = start.elapsed();
                println!(
                    "\n{} tokens generated ({:.2} token/s)\n",
                    output_token_ids.len(),
                    output_token_ids.len() as f64 / dt.as_secs_f64(),
                );
            }
        }
        None => {
            let mut model = builder.build_encoder()?;
            let sentences = [
                "The cat sits outside",
                "A man is playing guitar",
                "I love pasta",
                "The new movie is awesome",
                "The cat plays in the garden",
                "A woman watches TV",
                "The new movie is so great",
                "Do you like pizza?",
            ];
            let n_sentences = sentences.len();
            let mut all_embeddings = Vec::with_capacity(n_sentences);
            for sentence in sentences {
                let tokens = tokenizer
                    .encode(sentence, true)
                    .map_err(E::msg)?
                    .get_ids()
                    .to_vec();
                let token_ids = Tensor::new(&tokens[..], model.device())?.unsqueeze(0)?;
                let embeddings = model.forward(&token_ids)?;
                println!("generated embeddings {:?}", embeddings.shape());
                // Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
                let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
                let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
                let embeddings = if args.normalize_embeddings {
                    normalize_l2(&embeddings)?
                } else {
                    embeddings
                };
                println!("pooled embeddings {:?}", embeddings.shape());
                all_embeddings.push(embeddings)
            }

            let mut similarities = vec![];
            for (i, e_i) in all_embeddings.iter().enumerate() {
                for (j, e_j) in all_embeddings
                    .iter()
                    .enumerate()
                    .take(n_sentences)
                    .skip(i + 1)
                {
                    let sum_ij = (e_i * e_j)?.sum_all()?.to_scalar::<f32>()?;
                    let sum_i2 = (e_i * e_i)?.sum_all()?.to_scalar::<f32>()?;
                    let sum_j2 = (e_j * e_j)?.sum_all()?.to_scalar::<f32>()?;
                    let cosine_similarity = sum_ij / (sum_i2 * sum_j2).sqrt();
                    similarities.push((cosine_similarity, i, j))
                }
            }
            similarities.sort_by(|u, v| v.0.total_cmp(&u.0));
            for &(score, i, j) in similarities[..5].iter() {
                println!("score: {score:.2} '{}' '{}'", sentences[i], sentences[j])
            }
        }
    }
    Ok(())
}

pub fn normalize_l2(v: &Tensor) -> Result<Tensor> {
    Ok(v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)?)
}