You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: main.rs
    path of file: ./repos/candle/candle-examples/examples/mixtral
    
    the code of the file until where you have to start completion: #[cfg(feature = "mkl")]
extern crate intel_mkl_src;

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

use anyhow::{Error as E, Result};
use clap::Parser;

use candle_transformers::models::mixtral::{Config, Model};

use candle::{DType, Device, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;

struct TextGeneration {
    model: Model,
    device: Device,
    tokenizer: TokenOutputStream,
    logits_processor: LogitsProcessor,
    repeat_penalty: f32,
    repeat_last_n: usize,
}

impl TextGeneration {
    #[allow(clippy::too_many_arguments)]
    fn new(
        model: Model,
        tokenizer: Tokenizer,
        seed: u64,
        temp: Option<f64>,
        top_p: Option<f64>,
        repeat_penalty: f32,
        repeat_last_n: usize,
        device: &Device,
    ) -> Self {
        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
        Self {
            model,
            tokenizer: TokenOutputStream::new(tokenizer),
            logits_processor,
            repeat_penalty,
            repeat_last_n,
            device: device.clone(),
        }
    }

    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
        use std::io::Write;
        self.tokenizer.clear();
        let mut tokens = self
            .tokenizer
            .tokenizer()
            .encode(prompt, true)
            .map_err(E::msg)?
            .get_ids()
            .to_vec();
        for &t in tokens.iter() {
            if let Some(t) = self.tokenizer.next_token(t)? {
                print!("{t}")
            }
        }
        std::io::stdout().flush()?;

        let mut generated_tokens = 0usize;
        let eos_token = match self.tokenizer.get_token("</s>") {
            Some(token) => token,
            None => anyhow::bail!("cannot find the </s> token"),
        };
        let start_gen = std::time::Instant::now();
        for index in 0..sample_len {
            let context_size = if index > 0 { 1 } else { 
    
    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
        use std::io::Write;
        self.tokenizer.clear();
        let mut tokens = self
            .tokenizer
            .tokenizer()
            .encode(prompt, true)
            .map_err(E::msg)?
            .get_ids()
            .to_vec();
        for &t in tokens.iter() {
            if let Some(t) = self.tokenizer.next_token(t)? {
                print!("{t}")
            }
        }
        std::io::stdout().flush()?;

        let mut generated_tokens = 0usize;
        let eos_token = match self.tokenizer.get_token("</s>") {
            Some(token) => token,
            None => anyhow::bail!("cannot find the </s> token"),
        };
        let start_gen = std::time::Instant::now();
        for index in 0..sample_len {
            let context_size = if index > 0 { 1 } else { tokens.len() };
            let start_pos = tokens.len().saturating_sub(context_size);
            let ctxt = &tokens[start_pos..];
            let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
            let logits = self.model.forward(&input, start_pos)?;
            let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
            let logits = if self.repeat_penalty == 1. {
                logits
            } else {
                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
                candle_transformers::utils::apply_repeat_penalty(
                    &logits,
                    self.repeat_penalty,
                    &tokens[start_at..],
                )?
            };

            let next_token = self.logits_processor.sample(&logits)?;
            tokens.push(next_token);
            generated_tokens += 1;
            if next_token == eos_token {
                break;
            }
            if let Some(t) = self.tokenizer.next_token(next_token)? {
                print!("{t}");
                std::io::stdout().flush()?;
            }
        }
        let dt = start_gen.elapsed();
        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
            print!("{rest}");
        }
        std::io::stdout().flush()?;
        println!(
            "\n{generated_tokens} tokens generated ({:.2} token/s)",
            generated_tokens as f64 / dt.as_secs_f64(),
        );
        Ok(())
    }