use candle::{Device, Result, Tensor};

#[derive(Debug, Clone)]
pub struct Cache {
    // all_data is an option on a Tensor, this makes it possible to only create the actual tensor
    // on the first call where the batch size is easily known.
    // Also this makes it safe to clone a KvCache that has been reseted (as in it will not share
    // its internal state with the cloned instance).
    all_data: Option<Tensor>,
    dim: usize,
    current_seq_len: usize,
    max_seq_len: usize,
}

impl Cache {
    pub fn new(dim: usize, max_seq_len: usize) -> Self {
        Self {
            all_data: None,
            dim,
            current_seq_len: 0,
            max_seq_len,
        }
    }

    pub fn dim(&self) -> usize {
        self.dim
    }

    pub fn current_seq_len(&self) -> usize {
        self.current_seq_len
    }

    pub fn max_seq_len(&self) -> usize {
        self.max_seq_len
    }

    pub fn all_data(&self) -> &Option<Tensor> {
        &self.all_data
    }

    pub fn current_data(&self) -> Result<Option<Tensor>> {
        let data = match self.all_data.as_ref() {
            None => None,
            Some(d) => Some(d.narrow(self.dim, 0, self.current_seq_len)?),
        };
        Ok(data)
    }

    pub fn reset(&mut self) {
        self.current_seq_len = 0;
        self.all_data = None;
    }

    pub fn append(&mut self, src: &Tensor) -> Result<()> {
        let seq_len = src.dim(self.dim)?;
        // This doesn't seem very idiomatic but because the creation can fail, it's tricky to use
        // self.all_data.get_or_insert_with.
        if self.all_data.is_none() {
            let mut shape = src.dims().to_vec();
            shape[self.dim] = self.max_seq_len;
            let ad = Tensor::zeros(shape, src.dtype(), src.device())?;
            self.all_data = Some(ad)
        };
        let ad = self.all_data.as_mut().unwrap();
        if self.current_seq_len + seq_len > self.max_seq_len {
            candle::bail!(
                "kv-cache: above max-seq-len {}+{seq_len}>{}",
                self.current_seq_len,
                self.max_seq_len
            )
        }
        ad.slice_set(src, self.dim, self.current_seq_len)?;
        self.current_seq_len += seq_len;
        Ok(())
    }
}

#[derive(Debug, Clone)]
pub struct KvCache {
    k: Cache,
    v: Cache,
}

impl KvCache {
    pub fn new(dim: usize, max_seq_len: usize) -> Self {
        let k = Cache::new(dim, max_seq_len);
        let v = Cache::new(dim, max_seq_len);
        Self { k, v }
    }

    pub fn k_cache(&self) -> &Cache {
        &self.k
    }

    pub fn v_cache(&self) -> &Cache {
        &self.v
    }

    pub fn k_cache_mut(&mut self) -> &mut Cache {
        &mut self.k
    }

    pub fn v_cache_mut(&mut self) -> &mut Cache {
        &mut self.v
    }

    pub fn k(&self) -> Result<Option<Tensor>> {
        self.k.current_data()
    }

    pub fn v(&self) -> Result<Option<Tensor>> {
        self.v.current_data()
    }

    pub fn append(&mut self, k: &Tensor, v: &Tensor) -> Result<(Tensor, Tensor)> {
        self.k.append(k)?;
        self.v.append(v)?;
        let out_k = self.k.current_data()?;
        let out_v = self.v.current_data()?;
        let k = match out_k {
            None => {
                let mut shape = k.dims().to_vec();
                shape[self.k.dim] = 0;
                Tensor::zeros(shape, k.dtype(), k.device())?
            }
            Some(k) => k,
        };
        let v = match out_v {
            None => {
                let mut shape = v.dims().to_vec();
                shape[self.k.dim] = 0;
                Tensor::zeros(shape, v.dtype(), v.device())?
            }
            Some(v) => v,
        };
        Ok((k, v))
    }

    pub fn current_seq_len(&self) -> usize {
        self.k.current_seq_len()
    }

    pub fn reset(&mut self) {
        self.k.reset();
        self.v.reset();
    }
}

#[derive(Debug, Clone)]
pub struct RotatingCache {
    all_data: Option<Tensor>,
    dim: usize,
    // `offset` is the current write index in the buffer
    offset: usize,
    // The total size of the sequence seen so far.
    current_seq_len: usize,
    // max_seq_len is the size of the rotating buffer, it is actually allowed for the full
    // sequence to grow past this limit.
    max_seq_len: usize,
}

impl RotatingCache {
    pub fn new(dim: usize, max_seq_len: usize) -> Self {
        Self {
            all_data: None,
            dim,
            offset: 0,
            current_seq_len: 0,
            max_seq_len,
        }
    }

    pub fn offset(&self) -> usize {
        self.offset
    }

    pub fn dim(&self) -> usize {
        self.dim
    }

    pub fn current_seq_len(&self) -> usize {
        self.current_seq_len
    }

    pub fn max_seq_len(&self) -> usize {
        self.max_seq_len
    }

    pub fn all_data(&self) -> &Option<Tensor> {
        &self.all_data
    }

    pub fn current_data(&self) -> Result<Option<Tensor>> {
        let data = match self.all_data.as_ref() {
            None => None,
            Some(d) => {
                if self.current_seq_len >= self.max_seq_len {
                    Some(d.clone())
                } else {
                    Some(d.narrow(self.dim, 0, self.current_seq_len)?)
                }
            }
        };
        Ok(data)
    }

    pub fn reset(&mut self) {
        self.offset = 0;
        self.current_seq_len = 0;
        self.all_data = None;
    }

    pub fn append(&mut self, src: &Tensor) -> Result<Tensor> {
        let seq_len = src.dim(self.dim)?;
        // This doesn't seem very idiomatic but because the creation can fail, it's tricky to use
        // self.all_data.get_or_insert_with.
        if self.all_data.is_none() {
            let mut shape = src.dims().to_vec();
            shape[self.dim] = self.max_seq_len;
            let ad = Tensor::zeros(shape, src.dtype(), src.device())?;
            self.all_data = Some(ad)
        };
        let ad = self.all_data.as_mut().unwrap();

        self.current_seq_len += seq_len;
        if seq_len >= self.max_seq_len {
            let to_copy = src
                .narrow(self.dim, seq_len - self.max_seq_len, self.max_seq_len)?
                .contiguous()?;
            ad.slice_set(&to_copy, self.dim, 0)?;
            self.offset = 0;
            // Here we return `src` rather than `ad` so that all the past can be used.
            Ok(src.clone())
        } else {
            let rem_len = self.max_seq_len - self.offset;
            if seq_len <= rem_len {
                ad.slice_set(&src.contiguous()?, self.dim, self.offset)?;
                self.offset = (self.offset + seq_len) % self.max_seq_len;
            } else {
                // We have to make two copies here as we go over the boundary of the cache.
                if rem_len > 0 {
                    let src1 = src.narrow(self.dim, 0, rem_len)?.contiguous()?;
                    ad.slice_set(&src1, self.dim, self.offset)?;
                }
                let src2 = src
                    .narrow(self.dim, rem_len, seq_len - rem_len)?
                    .contiguous()?;
                ad.slice_set(&src2, self.dim, 0)?;
                self.offset = seq_len - rem_len;
            }
            if self.current_seq_len >= self.max_seq_len {
                Ok(ad.clone())
            } else {
                Ok(ad.narrow(self.dim, 0, self.current_seq_len)?)
            }
        }
    }

    fn get_mask_abs(&self, size1: usize, size2: usize, device: &Device) -> Result<Tensor> {
        let context = self.max_seq_len;
        let mask: Vec<_> = (0..size1)
            .flat_map(|i| {
                (0..size2).map(move |j| {
                    u8::from(size1 + j > size2 + i || size1 + j + context < size2 + i)
                })
            })
            .collect();
        Tensor::from_slice(&mask, (size1, size2), device)
    }

    fn get_mask_rel(&self, size1: usize, size2: usize, device: &Device) -> Result<Tensor> {
        let context = self.max_seq_len;
        let upd_offset = (self.offset + size1) % self.max_seq_len;
        let mask: Vec<_> = (0..size1)
            .flat_map(|pos_src| {
                // The absolute position of the elements that will get added to the cache.
                let pos_src = self.current_seq_len + pos_src;
                (0..size2).map(move |pos_cache_rel| {
                    // The absolute position of the cache elements after the addition.
                    let pos_cache = self.current_seq_len + size1 + pos_cache_rel - upd_offset;
                    let pos_cache = if pos_cache_rel < upd_offset {
                        pos_cache
                    } else {
                        pos_cache - self.max_seq_len
                    };
                    u8::from(pos_cache > pos_src || pos_cache + context < pos_src)
                })
            })
            .collect();
        Tensor::from_slice(&mask, (size1, size2), device)
    }

    /// Returns the attn_mask to be applied *after* adding `seq_len` to the cache.
    pub fn attn_mask(&self, seq_len: usize, device: &Device) -> Result<Option<Tensor>> {
        let mask = if seq_len == 1 {
            None
        } else {
            let mask = if seq_len < self.max_seq_len {
                let cache_out_len = (self.current_seq_len + seq_len).min(self.max_seq_len);
                self.get_mask_rel(seq_len, cache_out_len, device)?
            } else {
                self.get_mask_abs(seq_len, seq_len, device)?
            };
            Some(mask)
        };
        Ok(mask)
    }
}

#[derive(Debug, Clone)]
pub struct RotatingKvCache {
    k: RotatingCache,
    v: RotatingCache,
}

impl RotatingKvCache {
    pub fn new(dim: usize, max_seq_len: usize) -> Self {
        let k = RotatingCache::new(dim, max_seq_len);
        let v = RotatingCache::new(dim, max_seq_len);
        Self { k, v }
    }

    pub fn k_cache(&self) -> &RotatingCache {
        &self.k
    }

    pub fn v_cache(&self) -> &RotatingCache {
        &self.v
    }

    pub fn k_cache_mut(&mut self) -> &mut RotatingCache {
        &mut self.k
    }

    pub fn v_cache_mut(&mut self) -> &mut RotatingCache {
        &mut self.v
    }

    pub fn k(&self) -> Result<Option<Tensor>> {
        self.k.current_data()
    }

    pub fn v(&self) -> Result<Option<Tensor>> {
        self.v.current_data()
    }

    pub fn append(&mut self, k: &Tensor, v: &Tensor) -> Result<(Tensor, Tensor)> {
        let out_k = self.k.append(k)?;
        let out_v = self.v.append(v)?;
        Ok((out_k, out_v))
    }

    pub fn offset(&self) -> usize {
        self.k.offset()
    }

    pub fn current_seq_len(&self) -> usize {
        self.k.current_seq_len()
    }

    pub fn attn_mask(&self, seq_len: usize, device: &Device) -> Result<Option<Tensor>> {
        self.k.attn_mask(seq_len, device)
    }

    pub fn reset(&mut self) {
        self.k.reset();
        self.v.reset();
    }
}