# -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang

from typing import Optional

import torch
import triton
import triton.language as tl

from fla.ops.common.utils import prepare_chunk_indices


@triton.heuristics({
    'USE_OFFSETS': lambda args: args['offsets'] is not None
})
@triton.autotune(
    configs=[
        triton.Config({'BK': BK}, num_warps=num_warps, num_stages=num_stages)
        for BK in [32, 64, 128]
        for num_warps in [2, 4, 8]
        for num_stages in [2, 3, 4]
    ],
    key=['H', 'K', 'BT', 'USE_OFFSETS'],
)
@triton.jit(do_not_specialize=['T'])
def chunk_scaled_dot_kkt_fwd_kernel(
    k,
    beta,
    A,
    offsets,
    indices,
    T,
    H: tl.constexpr,
    K: tl.constexpr,
    BT: tl.constexpr,
    BK: tl.constexpr,
    HEAD_FIRST: tl.constexpr,
    USE_OFFSETS: tl.constexpr,
):
    i_t, i_bh = tl.program_id(0), tl.program_id(1)
    i_b, i_h = i_bh // H, i_bh % H
    if USE_OFFSETS:
        i_n, i_t = tl.load(indices + i_t * 2).to(tl.int32), tl.load(indices + i_t * 2 + 1).to(tl.int32)
        bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32)
        T = eos - bos
    else:
        bos, eos = i_b * T, i_b * T + T
    o_t = tl.arange(0, BT)

    if HEAD_FIRST:
        p_beta = tl.make_block_ptr(beta + i_bh * T, (T,), (1,), (i_t * BT,), (BT,), (0,))
    else:
        p_beta = tl.make_block_ptr(beta + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
    b_beta = tl.load(p_beta, boundary_check=(0,))

    b_A = tl.zeros([BT, BT], dtype=tl.float32)
    for i_k in range(tl.cdiv(K, BK)):
        if HEAD_FIRST:
            p_k = tl.make_block_ptr(k + i_bh * T*K, (T, K), (K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
        else:
            p_k = tl.make_block_ptr(k + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
        b_k = tl.load(p_k, boundary_check=(0, 1))
        b_kb = b_k * b_beta[:, None]
        b_A += tl.dot(b_kb.to(b_k.dtype), tl.trans(b_k))

    b_A = tl.where(o_t[:, None] > o_t[None, :], b_A, 0)
    if HEAD_FIRST:
        p_A = tl.make_block_ptr(A + i_bh * T*BT, (T, BT), (BT, 1), (i_t * BT, 0), (BT, BT), (1, 0))
    else:
        p_A = tl.make_block_ptr(A + (bos*H + i_h) * BT, (T, BT), (BT*H, 1), (i_t * BT, 0), (BT, BT), (1, 0))
    tl.store(p_A, b_A.to(p_A.dtype.element_ty), boundary_check=(0, 1))


def chunk_scaled_dot_kkt_fwd(
    k: torch.Tensor,
    beta: torch.Tensor,
    cu_seqlens: Optional[torch.LongTensor],
    head_first: bool = False,
    chunk_size: int = 64,
    output_dtype: torch.dtype = torch.float32
) -> torch.Tensor:
    r"""
    Compute beta * K * K^T.

    Args:
        k (torch.Tensor):
            The key tensor of shape `[B, T, H, K]` if not `head_first` else `[B, H, T, K]`.
        beta (torch.Tensor):
            The beta tensor of shape `[B, T, H]` if not `head_first` else `[B, H, T]`.
        cu_seqlens (torch.LongTensor):
            The cumulative sequence lengths of the input tensor.
            Default: None
        head_first (bool):
            If False, the input/output tensor is in the shape of `[B, T, H, K]`.
            If True, the input/output tensor is in the shape of `[B, H, T, K]`.
            Default: False
        chunk_size (int):
            The chunk size. Default: 64.
        output_dtype (torch.dtype):
            The dtype of the output tensor. Default: `torch.float32`

    Returns:
        beta * K * K^T of shape `[B, T, H, BT]` if not `head_first` else `[B, H, T, BT]`,
        where `BT` is the chunk size.
    """
    if head_first:
        B, H, T, K = k.shape
    else:
        B, T, H, K = k.shape
    BT = chunk_size
    indices = prepare_chunk_indices(cu_seqlens, BT) if cu_seqlens is not None else None
    NT = triton.cdiv(T, BT) if cu_seqlens is None else len(indices)
    A = torch.empty(B, *((H, T) if head_first else (T, H)), BT, device=k.device, dtype=output_dtype)
    chunk_scaled_dot_kkt_fwd_kernel[(NT, B * H)](
        k=k,
        beta=beta,
        A=A,
        offsets=cu_seqlens,
        indices=indices,
        T=T,
        H=H,
        K=K,
        BT=BT,
        HEAD_FIRST=head_first
    )
    return A