# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
#   https://github.com/fla-org/flash-linear-attention/graphs/contributors

import torch

from ...ops.attn.parallel import parallel_attn


def parallel_forgetting_attn(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    g: torch.Tensor,
    scale: float | None = None,
    window_size: int | None = None,
    cu_seqlens: torch.LongTensor | None = None,
    **kwargs
) -> torch.Tensor:
    r"""
    Args:
        q (torch.Tensor):
            queries of shape `[B, T, HQ, K]`.
        k (torch.Tensor):
            keys of shape `[B, T, H, K]`.
            GQA will be applied if HQ is divisible by H.
        v (torch.Tensor):
            values of shape `[B, T, H, V]`.
        g (torch.Tensor):
            log decay factors of shape `[B, T, HQ]`.
        scale (Optional[float]):
            Scale factor for attention scores.
            If not provided, it will default to `1 / sqrt(K)`. Default: `None`.
        window_size (Optional[int]):
            Sliding window size. If provided, each query at position i only attends to
            keys in `[i - window_size + 1, i]`. If `None`, full causal attention is used.
            Default: `None`.
        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, HQ, V]`.
    """
    if 'head_first' in kwargs:
        raise DeprecationWarning(
            "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.",
        )
    if scale is None:
        scale = k.shape[-1] ** -0.5
    if cu_seqlens is not None and q.shape[0] != 1:
        raise ValueError(
            f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`. "
            f"Please flatten variable-length inputs before processing.",
        )

    o = parallel_attn(q, k, v, g, scale, window_size=window_size, cu_seqlens=cu_seqlens)
    return o