# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang


import torch

from fla.ops.linear_attn.utils import normalize_output
from fla.ops.simple_gla import fused_chunk_simple_gla


@torch.compiler.disable
def fused_chunk_linear_attn(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    scale: float | None = None,
    initial_state: torch.Tensor | None = None,
    output_final_state: bool = False,
    normalize: bool = True,
    cu_seqlens: torch.LongTensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
    r"""
    Args:
        q (torch.Tensor):
            queries of shape `[B, T, H, K]`.
        k (torch.Tensor):
            keys of shape `[B, T, H, K]`.
        v (torch.Tensor):
            values of shape `[B, T, H, V]`.
        scale (Optional[float]):
            Scale factor for linear attention scores.
            If not provided, it will default to `1 / sqrt(K)`. Default: `None`.
        initial_state (Optional[torch.Tensor]):
            Initial state of shape `[B, H, K, V]`. Default: `None`.
        output_final_state (Optional[bool]):
            Whether to output the final state of shape `[B, H, K, V]`. Default: `False`.
        normalize (bool):
            Whether to normalize the output. Default: `True`.
        cu_seqlens (torch.LongTensor):
            Cumulative sequence lengths of shape `[N+1]` used for variable-length training,
            consistent with the FlashAttention API.

    Returns:
        o (torch.Tensor):
            Outputs of shape `[B, T, H, V]`.
        final_state (torch.Tensor):
            Final state of shape `[B, H, K, V]` if `output_final_state=True` else `None`
    """
    o, final_state = fused_chunk_simple_gla(
        q=q,
        k=k,
        v=v,
        scale=scale,
        initial_state=initial_state,
        output_final_state=output_final_state,
        cu_seqlens=cu_seqlens,
    )
    if normalize:
        o = normalize_output(q * scale, k, o)
    return o, final_state