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

from typing import Optional, Tuple

import torch
import triton
import triton.language as tl

from fla.ops.common.utils import prepare_chunk_offsets
from fla.ops.utils.op import exp
from fla.utils import check_shared_mem, use_cuda_graph


@triton.heuristics({
    'USE_FINAL_STATE_GRADIENT': lambda args: args['dht'] is not None,
    'USE_INITIAL_STATE': lambda args: args['dh0'] is not None,
    'USE_OFFSETS': lambda args: args['offsets'] is not None,
})
@triton.autotune(
    configs=[
        triton.Config({}, num_warps=num_warps, num_stages=num_stages)
        for num_warps in [2, 4, 8, 16, 32]
        for num_stages in [2, 3, 4]
    ],
    key=['BT', 'BK', 'BV', "V"],
    use_cuda_graph=use_cuda_graph,
)
@triton.jit(do_not_specialize=['T'])
def chunk_dplr_bwd_kernel_dhu(
    qg,
    bg,
    w,
    gk,
    dht,
    dh0,
    do,
    dh,
    dv,
    dv2,
    offsets,
    chunk_offsets,
    T,
    H: tl.constexpr,
    K: tl.constexpr,
    V: tl.constexpr,
    BT: tl.constexpr,
    BC: tl.constexpr,
    BK: tl.constexpr,
    BV: tl.constexpr,
    USE_FINAL_STATE_GRADIENT: tl.constexpr,
    USE_INITIAL_STATE: tl.constexpr,
    USE_OFFSETS: tl.constexpr,
    HEAD_FIRST: tl.constexpr
):
    i_k, i_v, i_nh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
    i_n, i_h = i_nh // H, i_nh % H
    if USE_OFFSETS:
        bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32)
        T = eos - bos
        NT = tl.cdiv(T, BT)
        boh = tl.load(chunk_offsets + i_n).to(tl.int32)
    else:
        bos, eos = i_n * T, i_n * T + T
        NT = tl.cdiv(T, BT)
        boh = i_n * NT

    # [BK, BV]
    b_dh = tl.zeros([BK, BV], dtype=tl.float32)
    if USE_FINAL_STATE_GRADIENT:
        p_dht = tl.make_block_ptr(dht + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
        b_dh += tl.load(p_dht, boundary_check=(0, 1))

    mask_k = tl.arange(0, BK) < K
    for i_t in range(NT - 1, -1, -1):
        if HEAD_FIRST:
            p_dh = tl.make_block_ptr(dh + (i_nh * NT + i_t) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
        else:
            p_dh = tl.make_block_ptr(dh + ((boh+i_t) * H + i_h) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
        tl.store(p_dh, b_dh.to(p_dh.dtype.element_ty), boundary_check=(0, 1))
        b_dh_tmp = tl.zeros([BK, BV], dtype=tl.float32)
        for i_c in range(tl.cdiv(BT, BC) - 1, -1, -1):
            if HEAD_FIRST:
                p_qg = tl.make_block_ptr(qg + i_nh * T*K, (K, T), (1, K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1))
                p_bg = tl.make_block_ptr(bg + i_nh * T*K, (T, K), (K, 1), (i_t * BT + i_c * BC, i_k * BK), (BC, BK), (1, 0))
                p_w = tl.make_block_ptr(w + i_nh * T*K, (K, T), (1, K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1))
                p_dv = tl.make_block_ptr(dv + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
                p_do = tl.make_block_ptr(do + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
                p_dv2 = tl.make_block_ptr(dv2 + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
            else:
                p_qg = tl.make_block_ptr(qg+(bos*H+i_h)*K, (K, T), (1, H*K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1))
                p_bg = tl.make_block_ptr(bg+(bos*H+i_h)*K, (T, K), (H*K, 1), (i_t * BT + i_c * BC, i_k * BK), (BC, BK), (1, 0))
                p_w = tl.make_block_ptr(w+(bos*H+i_h)*K, (K, T), (1, H*K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1))
                p_dv = tl.make_block_ptr(dv+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
                p_do = tl.make_block_ptr(do+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
                p_dv2 = tl.make_block_ptr(dv2+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
            # [BK, BT]
            b_qg = tl.load(p_qg, boundary_check=(0, 1))
            # [BT, BK]
            b_bg = tl.load(p_bg, boundary_check=(0, 1))
            b_w = tl.load(p_w, boundary_check=(0, 1))
            # [BT, V]
            b_do = tl.load(p_do, boundary_check=(0, 1))
            b_dv = tl.load(p_dv, boundary_check=(0, 1))
            b_dv2 = b_dv + tl.dot(b_bg, b_dh.to(b_bg.dtype))
            tl.store(p_dv2, b_dv2.to(p_dv.dtype.element_ty), boundary_check=(0, 1))
            # [BK, BV]
            b_dh_tmp += tl.dot(b_qg, b_do.to(b_qg.dtype))
            b_dh_tmp += tl.dot(b_w, b_dv2.to(b_qg.dtype))
        last_idx = min((i_t + 1) * BT, T) - 1
        if HEAD_FIRST:
            bg_last = tl.load(gk + (i_nh * T + last_idx) * K + tl.arange(0, BK), mask=mask_k)
        else:
            bg_last = tl.load(gk + ((bos + last_idx) * H + i_h) * K + tl.arange(0, BK), mask=mask_k)
        b_dh *= exp(bg_last)[:, None]
        b_dh += b_dh_tmp

    if USE_INITIAL_STATE:
        p_dh0 = tl.make_block_ptr(dh0 + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
        tl.store(p_dh0, b_dh.to(p_dh0.dtype.element_ty), boundary_check=(0, 1))


def chunk_dplr_bwd_dhu(
    qg: torch.Tensor,
    bg: torch.Tensor,
    w: torch.Tensor,
    gk: torch.Tensor,
    h0: torch.Tensor,
    dht: Optional[torch.Tensor],
    do: torch.Tensor,
    dv: torch.Tensor,
    offsets: Optional[torch.LongTensor] = None,
    indices: Optional[torch.LongTensor] = None,
    head_first: bool = True,
    chunk_size: int = 64
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    if head_first:
        B, H, T, K, V = *qg.shape, do.shape[-1]
    else:
        B, T, H, K, V = *qg.shape, do.shape[-1]
    BT = min(chunk_size, max(triton.next_power_of_2(T), 16))
    BK = triton.next_power_of_2(K)
    assert BK <= 256, "current kernel does not support head dimension being larger than 256."
    # H100
    if check_shared_mem('hopper', qg.device.index):
        BV = 64
        BC = 64 if K <= 128 else 32
    elif check_shared_mem('ampere', qg.device.index):  # A100
        BV = 32
        BC = 32
    else:  # Etc: 4090
        BV = 16
        BC = 16

    # N: the actual number of sequences in the batch with either equal or variable lengths
    if offsets is None:
        N, NT, chunk_offsets = B, triton.cdiv(T, BT), None
    else:
        N, NT, chunk_offsets = len(offsets) - 1, len(indices), prepare_chunk_offsets(offsets, BT)

    BC = min(BT, BC)
    NK, NV = triton.cdiv(K, BK), triton.cdiv(V, BV)
    assert NK == 1, 'NK > 1 is not supported because it involves time-consuming synchronization'

    if head_first:
        dh = qg.new_empty(B, H, NT, K, V)
    else:
        dh = qg.new_empty(B, NT, H, K, V)
    dh0 = torch.empty_like(h0, dtype=torch.float32) if h0 is not None else None
    dv2 = torch.zeros_like(dv)

    grid = (NK, NV, N * H)
    chunk_dplr_bwd_kernel_dhu[grid](
        qg=qg,
        bg=bg,
        w=w,
        gk=gk,
        dht=dht,
        dh0=dh0,
        do=do,
        dh=dh,
        dv=dv,
        dv2=dv2,
        offsets=offsets,
        chunk_offsets=chunk_offsets,
        T=T,
        H=H,
        K=K,
        V=V,
        BT=BT,
        BC=BC,
        BK=BK,
        BV=BV,
        HEAD_FIRST=head_first
    )
    return dh, dh0, dv2