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

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

use clap::{Parser, ValueEnum};
use std::io::Write;
use tokenizers::Tokenizer;

use candle::quantized::gguf_file;
use candle::Tensor;
use candle_transformers::generation::{LogitsProcessor, Sampling};

use candle_examples::token_output_stream::TokenOutputStream;
use candle_transformers::models::quantized_qwen2::ModelWeights as Qwen2;

const DEFAULT_PROMPT: &str = "Write a function to count prime numbers up to N. ";

#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
enum Which {
    #[value(name = "0.5b")]
    W2_0_5b,
    #[value(name = "1.5b")]
    W2_1_5b,
    #[value(name = "7b")]
    W2_7b,
    #[value(name = "72b")]
    W2_72b,
}

#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
    /// GGUF file to load, typically a .gguf file generated by the quantize command from llama.cpp
    #[arg(long)]
    model: Option<String>,

    /// The initial prompt, use 'interactive' for entering multiple prompts in an interactive way
    /// and 'chat' for an interactive model where history of previous prompts and generated tokens
    /// is preserved.
    #[arg(long)]
    prompt: Option<String>,

    /// The length of the sample to generate (in tokens).
    #[arg(short = 'n', long, default_value_t = 1000)]
    sample_len: usize,

    /// The tokenizer config in json format.
    #[arg(long)]
    tokenizer: Option<String>,

    /// The temperature used to generate samples, use 0 for greedy sampling.
    #[arg(long, default_value_t = 0.8)]
    temperature: f64,

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

    /// Only sample among the top K samples.
    #[arg(long)]
    top_k: Option<usize>,

    /// The seed to use when generating random samples.
    #[arg(long, default_value_t = 299792458)]
    seed: u64,

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

    /// Process prompt elements separately.
    #[arg(long)]
    split_prompt: bool,

    /// Run on CPU rather than GPU even if a GPU is available.
    #[arg(long)]
    cpu: bool,

    /// 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 size to use.
    #[arg(long, default_value = "0.5b")]
    which: Which,
}

impl Args {
    fn tokenizer(&self) -> anyhow::Result<Tokenizer> {
        let tokenizer_path = match &self.tokenizer {
            Some(config) => std::path::PathBuf::from(config),
            None => {
                let api = hf_hub::api::sync::Api::new()?;
                let repo = match self.which {
                    Which::W2_0_5b => "Qwen/Qwen2-0.5B-Instruct",
                    Which::W2_1_5b => "Qwen/Qwen2-1.5B-Instruct",
                    Which::W2_7b => "Qwen/Qwen2-7B-Instruct",
                    Which::W2_72b => "Qwen/Qwen2-72B-Instruct",
                };
                let api = api.model(repo.to_string());
                api.get("tokenizer.json")?
            }
        };
        Tokenizer::from_file(tokenizer_path).map_err(anyhow::Error::msg)
    }

    fn model(&self) -> anyhow::Result<std::path::PathBuf> {
        let model_path = match &self.model {
            Some(config) => std::path::PathBuf::from(config),
            None => {
                let (repo, filename, revision) = match self.which {
                    Which::W2_0_5b => (
                        "Qwen/Qwen2-0.5B-Instruct-GGUF",
                        "qwen2-0_5b-instruct-q4_0.gguf",
                        "main",
                    ),
                    Which::W2_1_5b => (
                        "Qwen/Qwen2-1.5B-Instruct-GGUF",
                        "qwen2-1_5b-instruct-q4_0.gguf",
                        "main",
                    ),
                    Which::W2_7b => (
                        "Qwen/Qwen2-7B-Instruct-GGUF",
                        "qwen2-7b-instruct-q4_0.gguf",
                        "main",
                    ),
                    Which::W2_72b => (
                        "Qwen/Qwen2-72B-Instruct-GGUF",
                        "qwen2-72b-instruct-q4_0.gguf",
                        "main",
                    ),
                };
                let api = hf_hub::api::sync::Api::new()?;
                api.repo(hf_hub::Repo::with_revision(
                    repo.to_string(),
                    hf_hub::RepoType::Model,
                    revision.to_string(),
                ))
                .get(filename)?
            }
        };
        Ok(model_path)
    }
}

fn format_size(size_in_bytes: usize) -> String {
    if size_in_bytes < 1_000 {
        format!("{}B", size_in_bytes)
    } else if size_in_bytes < 1_000_000 {
        format!("{:.2}KB", size_in_bytes as f64 / 1e3)
    } else if size_in_bytes < 1_000_000_000 {
        format!("{:.2}MB", size_in_bytes as f64 / 1e6)
    } else {
        format!("{:.2}GB", size_in_bytes as f64 / 1e9)
    }
}

fn main() -> anyhow::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
    };

    println!(
        "avx: {}, neon: {}, simd128: {}, f16c: {}",
        candle::utils::with_avx(),
        candle::utils::with_neon(),
        candle::utils::with_simd128(),
        candle::utils::with_f16c()
    );
    println!(
        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
        args.temperature, args.repeat_penalty, args.repeat_last_n
    );

    let model_path = args.model()?;
    let mut file = std::fs::File::open(&model_path)?;
    let start = std::time::Instant::now();
    let device = candle_examples::device(args.cpu)?;

    let mut model = {
        let model = gguf_file::Content::read(&mut file).map_err(|e| e.with_path(model_path))?;
        let mut total_size_in_bytes = 0;
        for (_, tensor) in model.tensor_infos.iter() {
            let elem_count = tensor.shape.elem_count();
            total_size_in_bytes +=
                elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.block_size();
        }
        println!(
            "loaded {:?} tensors ({}) in {:.2}s",
            model.tensor_infos.len(),
            &format_size(total_size_in_bytes),
            start.elapsed().as_secs_f32(),
        );
        Qwen2::from_gguf(model, &mut file, &device)?
    };
    println!("model built");

    let tokenizer = args.tokenizer()?;
    let mut tos = TokenOutputStream::new(tokenizer);
    let prompt_str = args.prompt.unwrap_or_else(|| DEFAULT_PROMPT.to_string());
    let prompt_str = format!(
        "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n",
        prompt_str
    );
    print!("formatted instruct prompt: {}", &prompt_str);
    let tokens = tos
        .tokenizer()
        .encode(prompt_str, true)
        .map_err(anyhow::Error::msg)?;
    let tokens = tokens.get_ids();
    let to_sample = args.sample_len.saturating_sub(1);
    let mut all_tokens = vec![];
    let mut logits_processor = {
        let temperature = args.temperature;
        let sampling = if temperature <= 0. {
            Sampling::ArgMax
        } else {
            match (args.top_k, args.top_p) {
                (None, None) => Sampling::All { temperature },
                (Some(k), None) => Sampling::TopK { k, temperature },
                (None, Some(p)) => Sampling::TopP { p, temperature },
                (Some(k), Some(p)) => Sampling::TopKThenTopP { k, p, temperature },
            }
        };
        LogitsProcessor::from_sampling(args.seed, sampling)
    };
    let start_prompt_processing = std::time::Instant::now();
    let mut next_token = if !args.split_prompt {
        let input = Tensor::new(tokens, &device)?.unsqueeze(0)?;
        let logits = model.forward(&input, 0)?;
        let logits = logits.squeeze(0)?;
        logits_processor.sample(&logits)?
    } else {
        let mut next_token = 0;
        for (pos, token) in tokens.iter().enumerate() {
            let input = Tensor::new(&[*token], &device)?.unsqueeze(0)?;
            let logits = model.forward(&input, pos)?;
            let logits = logits.squeeze(0)?;
            next_token = logits_processor.sample(&logits)?
        }
        next_token
    };
    let prompt_dt = start_prompt_processing.elapsed();
    all_tokens.push(next_token);
    if let Some(t) = tos.next_token(next_token)? {
        print!("{t}");
        std::io::stdout().flush()?;
    }
    let eos_token = *tos.tokenizer().get_vocab(true).get("<|im_end|>").unwrap();
    let start_post_prompt = std::time::Instant::now();
    let mut sampled = 0;
    for index in 0..to_sample {
        let input = Tensor::new(&[next_token], &device)?.unsqueeze(0)?;
        let logits = model.forward(&input, tokens.len() + index)?;
        let logits = logits.squeeze(0)?;
        let logits = if args.repeat_penalty == 1. {
            logits
        } else {
            let start_at = all_tokens.len().saturating_sub(args.repeat_last_n);
            candle_transformers::utils::apply_repeat_penalty(
                &logits,
                args.repeat_penalty,
                &all_tokens[start_at..],
            )?
        };
        next_token = logits_processor.sample(&logits)?;
        all_tokens.push(next_token);
        if let Some(t) = tos.next_token(next_token)? {
            print!("{t}");
            std::io::stdout().flush()?;
        }
        sampled += 1;
        if next_token == eos_token {
            break;
        };
    }
    if let Some(rest) = tos.decode_rest().map_err(candle::Error::msg)? {
        print!("{rest}");
    }
    std::io::stdout().flush()?;
    let dt = start_post_prompt.elapsed();
    println!(
        "\n\n{:4} prompt tokens processed: {:.2} token/s",
        tokens.len(),
        tokens.len() as f64 / prompt_dt.as_secs_f64(),
    );
    println!(
        "{sampled:4} tokens generated: {:.2} token/s",
        sampled as f64 / dt.as_secs_f64(),
    );
    Ok(())
}