# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
#
# This file contains code copied from the flash-linear-attention project.
# The original source code was licensed under the MIT license and included
# the following copyright notice:
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
# ruff: noqa: E501
# mypy: ignore-errors
from typing import Optional

import torch
from vllm.triton_utils import tl, triton

from .utils import prepare_chunk_indices


@triton.heuristics({
    'HAS_SCALE': lambda args: args['scale'] is not None,
    'IS_VARLEN': lambda args: args['cu_seqlens'] is not None
})
@triton.jit(do_not_specialize=['T'])
def chunk_local_cumsum_scalar_kernel(
    s,
    o,
    scale,
    cu_seqlens,
    chunk_indices,
    T,
    B: tl.constexpr,
    H: tl.constexpr,
    BLOCK_T: tl.constexpr,
    REVERSE: tl.constexpr,
    HAS_SCALE: tl.constexpr,
    IS_VARLEN: tl.constexpr,
    HEAD_FIRST: tl.constexpr,
    CHUNK_SIZE: tl.constexpr = 64,
):
    i_block, i_b = tl.program_id(0), tl.program_id(1)
    N_CHUNKS: tl.constexpr = BLOCK_T // CHUNK_SIZE

    if IS_VARLEN:
        i_s, i_block = tl.load(chunk_indices + i_block * 2).to(
            tl.int32), tl.load(chunk_indices + i_block * 2 + 1).to(tl.int32)
        bos, eos = tl.load(cu_seqlens + i_s).to(
            tl.int32), tl.load(cu_seqlens + i_s + 1).to(tl.int32)
        T = eos - bos
    else:
        bos, eos = i_b * T, i_b * T + T

    if HEAD_FIRST:
        ptr_s = tl.make_block_ptr(s + bos * H, (H, T), (T, 1),
                                  (0, i_block * BLOCK_T), (H, BLOCK_T), (1, 0))
        ptr_o = tl.make_block_ptr(o + bos * H, (H, T), (T, 1),
                                  (0, i_block * BLOCK_T), (H, BLOCK_T), (1, 0))
        b_s = tl.load(ptr_s, boundary_check=(0, )).to(tl.float32)
        b_s = tl.reshape(b_s, (H, N_CHUNKS, CHUNK_SIZE))
        b_s = tl.trans(b_s, (2, 0, 1))
        b_o = tl.cumsum(b_s, axis=0, reverse=REVERSE)
        if HAS_SCALE:
            b_o *= scale
        b_o = tl.trans(b_o, (2, 0, 1))
        b_o = tl.reshape(b_o, (H, BLOCK_T))
    else:
        ptr_s = tl.make_block_ptr(s + bos * H, (T, H), (H, 1),
                                  (i_block * BLOCK_T, 0), (BLOCK_T, H), (1, 0))
        ptr_o = tl.make_block_ptr(o + bos * H, (T, H), (H, 1),
                                  (i_block * BLOCK_T, 0), (BLOCK_T, H), (1, 0))
        b_s = tl.load(ptr_s, boundary_check=(0, )).to(tl.float32)
        b_s = tl.reshape(b_s, (N_CHUNKS, CHUNK_SIZE, H))
        b_s = tl.trans(b_s, (1, 0, 2))
        b_o = tl.cumsum(b_s, axis=0, reverse=REVERSE)
        if HAS_SCALE:
            b_o *= scale
        b_o = tl.trans(b_o, (1, 0, 2))
        b_o = tl.reshape(b_o, (BLOCK_T, H))

    tl.store(ptr_o, b_o.to(s.dtype.element_ty), boundary_check=(0, ))
    return


def chunk_local_cumsum_scalar(
    g,
    chunk_size,
    reverse: bool = False,
    scale: float = None,
    cu_seqlens: Optional[torch.Tensor] = None,
    head_first: bool = False,
    output_dtype: Optional[torch.Tensor] = torch.float,
):
    if head_first:
        B, H, T = g.shape
    else:
        B, T, H = g.shape
    assert chunk_size == 2**(chunk_size.bit_length() -
                             1), "chunk_size must be a power of 2"
    OPTIM_BLOCK_SIZE = triton.next_power_of_2((2**18) // (H * chunk_size))
    block_indices = prepare_chunk_indices(
        cu_seqlens,
        chunk_size=OPTIM_BLOCK_SIZE) if cu_seqlens is not None else None
    num_blocks = len(block_indices) if cu_seqlens is not None else triton.cdiv(
        T, OPTIM_BLOCK_SIZE)
    g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype)
    grid = (num_blocks, B)
    chunk_local_cumsum_scalar_kernel[grid](s=g_org,
                                           o=g,
                                           scale=scale,
                                           cu_seqlens=cu_seqlens,
                                           chunk_indices=block_indices,
                                           T=T,
                                           B=B,
                                           H=H,
                                           BLOCK_T=OPTIM_BLOCK_SIZE,
                                           CHUNK_SIZE=chunk_size,
                                           HEAD_FIRST=head_first,
                                           REVERSE=reverse,
                                           num_warps=8,
                                           num_stages=3)
    return g


def chunk_local_cumsum(g: torch.Tensor,
                       chunk_size: int,
                       reverse: bool = False,
                       scale: float = None,
                       cu_seqlens: Optional[torch.Tensor] = None,
                       head_first: bool = False,
                       output_dtype: Optional[torch.dtype] = torch.float,
                       **kwargs) -> torch.Tensor:
    if cu_seqlens is not None:
        assert g.shape[
            0] == 1, "Only batch size 1 is supported when cu_seqlens are provided"
    if len(g.shape) == 3:
        return chunk_local_cumsum_scalar(g=g,
                                         chunk_size=chunk_size,
                                         reverse=reverse,
                                         scale=scale,
                                         cu_seqlens=cu_seqlens,
                                         head_first=head_first,
                                         output_dtype=output_dtype)
    else:
        raise ValueError(f"Unsupported input shape {g.shape}, "
                         f"which should be (B, T, H, D) if `head_first=False` "
                         f"or (B, H, T, D) otherwise")