# 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.simple_gla.parallel import parallel_simple_gla


def parallel_retention(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    scale: float | None = None,
    output_attentions: bool = False,
    cu_seqlens: torch.LongTensor | None = None,
    **kwargs,
) -> 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 attention scores.
            If not provided, it will default to `1 / sqrt(K)`. Default: `None`.
        output_attentions (bool):
            Whether to output the materialized attention scores of shape `[B, H, T, T]`. Default: `False`.
        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]`.
        attn (torch.Tensor):
            Attention scores of shape `[B, H, T, T]` if `output_attentions=True` else `None`.
    """
    if 'head_first' in kwargs:
        raise DeprecationWarning(
            "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.",
        )
    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.",
        )
    s = (1 - q.new_tensor(2., dtype=torch.float).pow(-5. - q.new_tensor(range(q.shape[2]), dtype=torch.float))).log()
    g = s[None, None, :].expand(q.shape[0], q.shape[1], q.shape[2])

    o, attn = parallel_simple_gla(
        q=q,
        k=k,
        v=v,
        scale=scale,
        g=g,
        output_attentions=output_attentions,
        cu_seqlens=cu_seqlens,
    )
    return o, attn