# -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, <CENSORED>, <CENSORED>

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_INITIAL_STATE': lambda args: args['h0'] is not None,
    'STORE_FINAL_STATE': lambda args: args['ht'] 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'],
    use_cuda_graph=use_cuda_graph,
)
@triton.jit(do_not_specialize=['T'])
def chunk_dplr_fwd_kernel_h(
    kg,
    v,
    w,
    bg,
    u,
    v_new,
    gk,
    h,
    h0,
    ht,
    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,
    NT: tl.constexpr,
    USE_INITIAL_STATE: tl.constexpr,
    STORE_FINAL_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_h = tl.zeros([BK, BV], dtype=tl.float32)
    if USE_INITIAL_STATE:
        p_h0 = tl.make_block_ptr(h0 + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
        b_h = tl.load(p_h0, boundary_check=(0, 1)).to(tl.float32)

    for i_t in range(NT):
        if HEAD_FIRST:
            p_h = tl.make_block_ptr(h + (i_nh * NT + i_t) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
        else:
            p_h = tl.make_block_ptr(h + ((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_h, b_h.to(p_h.dtype.element_ty), boundary_check=(0, 1))

        b_hc = tl.zeros([BK, BV], dtype=tl.float32)
        # since we need to make all DK in the SRAM. we face serve SRAM memory burden. By subchunking we allievate such burden
        for i_c in range(tl.cdiv(min(BT, T - i_t * BT), BC)):
            if HEAD_FIRST:
                p_kg = tl.make_block_ptr(kg + 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, (K, T), (1, K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1))
                p_w = tl.make_block_ptr(w + i_nh * T*K, (T, K), (K, 1), (i_t * BT + i_c * BC, i_k * BK), (BC, BK), (1, 0))
                p_v = tl.make_block_ptr(v + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
                p_u = tl.make_block_ptr(u + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
                p_v_new = tl.make_block_ptr(v_new+i_nh*T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
            else:
                p_kg = tl.make_block_ptr(kg+(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, (K, T), (1, H*K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1))
                p_w = tl.make_block_ptr(w+(bos*H+i_h)*K, (T, K), (H*K, 1), (i_t * BT + i_c * BC, i_k * BK), (BC, BK), (1, 0))
                p_v = tl.make_block_ptr(v+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
                p_u = tl.make_block_ptr(u+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
                p_v_new = tl.make_block_ptr(v_new+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT+i_c*BC, i_v * BV), (BC, BV), (1, 0))
            # [BK, BC]
            b_kg = tl.load(p_kg, boundary_check=(0, 1))
            b_v = tl.load(p_v, boundary_check=(0, 1))
            b_w = tl.load(p_w, boundary_check=(0, 1))
            b_bg = tl.load(p_bg, boundary_check=(0, 1))
            b_v2 = tl.dot(b_w, b_h.to(b_w.dtype)) + tl.load(p_u, boundary_check=(0, 1))
            b_hc += tl.dot(b_kg, b_v)
            b_hc += tl.dot(b_bg.to(b_hc.dtype), b_v2)
            tl.store(p_v_new, b_v2.to(p_v_new.dtype.element_ty), boundary_check=(0, 1))

        last_idx = min((i_t + 1) * BT, T) - 1
        if HEAD_FIRST:
            b_g_last = tl.load(gk + i_nh * T * K + last_idx * K + tl.arange(0, BK), mask=tl.arange(0, BK) < K).to(tl.float32)
        else:
            b_g_last = tl.load(gk + (bos + last_idx) * H * K + i_h * K +
                               tl.arange(0, BK), mask=tl.arange(0, BK) < K).to(tl.float32)
        b_h *= exp(b_g_last[:, None])
        b_h += b_hc

    if STORE_FINAL_STATE:
        p_ht = tl.make_block_ptr(ht + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
        tl.store(p_ht, b_h.to(p_ht.dtype.element_ty, fp_downcast_rounding="rtne"), boundary_check=(0, 1))


def chunk_dplr_fwd_h(
    kg: torch.Tensor,
    v: torch.Tensor,
    w: torch.Tensor,
    u: torch.Tensor,
    bg: torch.Tensor,
    gk: torch.Tensor,
    initial_state: Optional[torch.Tensor] = None,
    output_final_state: bool = False,
    offsets: Optional[torch.LongTensor] = None,
    indices: Optional[torch.LongTensor] = None,
    head_first: bool = True,
    chunk_size: int = 64
) -> Tuple[torch.Tensor, torch.Tensor]:
    if head_first:
        B, H, T, K, V = *kg.shape, u.shape[-1]
    else:
        B, T, H, K, V = *kg.shape, u.shape[-1]
    BT = min(chunk_size, max(triton.next_power_of_2(T), 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)
    BK = triton.next_power_of_2(K)
    assert BK <= 256, "current kernel does not support head dimension larger than 256."
    # H100 can have larger block size

    if check_shared_mem('hopper', kg.device.index):
        BV = 64
        BC = 64 if K <= 128 else 32
    elif check_shared_mem('ampere', kg.device.index):  # A100
        BV = 32
        BC = 32
    else:
        BV = 16
        BC = 16

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

    if head_first:
        h = kg.new_empty(B, H, NT, K, V)
    else:
        h = kg.new_empty(B, NT, H, K, V)
    final_state = kg.new_empty(N, H, K, V, dtype=torch.float32) if output_final_state else None
    v_new = torch.empty_like(u)
    grid = (NK, NV, N * H)
    chunk_dplr_fwd_kernel_h[grid](
        kg=kg,
        v=v,
        w=w,
        bg=bg,
        u=u,
        v_new=v_new,
        h=h,
        gk=gk,
        h0=initial_state,
        ht=final_state,
        offsets=offsets,
        chunk_offsets=chunk_offsets,
        T=T,
        H=H,
        K=K,
        V=V,
        BT=BT,
        BC=BC,
        BK=BK,
        BV=BV,
        NT=NT,
        HEAD_FIRST=head_first
    )
    return h, v_new, final_state
