"""FlashAttention-2 backend for PaDoc's packed tree visibility pattern."""

from __future__ import annotations

import torch

# Transformers treats custom names containing "flash_attention" as Hub kernels.
ATTN_IMPLEMENTATION = "padoc_tree_fa2"


def tree_flash_attention_forward(
    module: torch.nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    dropout: float = 0.0,
    scaling: float | None = None,
    *,
    tree_q_indices: torch.LongTensor,
    tree_kv_indices: torch.LongTensor,
    tree_cu_seqlens_q: torch.IntTensor,
    tree_cu_seqlens_kv: torch.IntTensor,
    tree_max_seqlen_q: int | torch.Tensor,
    tree_max_seqlen_kv: int | torch.Tensor,
    **kwargs,
) -> tuple[torch.Tensor, None]:
    """Run exact tree attention as a virtual FlashAttention varlen batch."""

    del module, attention_mask, kwargs
    if tree_cu_seqlens_q.dtype != torch.int32 or tree_cu_seqlens_kv.dtype != torch.int32:
        raise TypeError("FlashAttention cumulative sequence lengths must be torch.int32.")
    try:
        from flash_attn import flash_attn_varlen_func
    except ImportError as exc:  # pragma: no cover - requires a CUDA environment
        raise ImportError(
            f"{ATTN_IMPLEMENTATION} requires the optional 'flash-attn' package."
        ) from exc

    batch_size, num_heads, sequence_length, head_dim = query.shape
    num_kv_heads = key.shape[1]
    query_flat = query.transpose(1, 2).reshape(batch_size * sequence_length, num_heads, head_dim)
    key_flat = key.transpose(1, 2).reshape(batch_size * sequence_length, num_kv_heads, head_dim)
    value_flat = value.transpose(1, 2).reshape(batch_size * sequence_length, num_kv_heads, head_dim)

    output_packed = flash_attn_varlen_func(
        query_flat.index_select(0, tree_q_indices),
        key_flat.index_select(0, tree_kv_indices),
        value_flat.index_select(0, tree_kv_indices),
        tree_cu_seqlens_q,
        tree_cu_seqlens_kv,
        int(tree_max_seqlen_q),
        int(tree_max_seqlen_kv),
        dropout_p=dropout,
        softmax_scale=scaling,
        causal=True,
    )

    output_flat = torch.zeros_like(query_flat)
    output_flat.index_copy_(0, tree_q_indices, output_packed)
    return output_flat.view(batch_size, sequence_length, num_heads, head_dim), None


def register_tree_flash_attention() -> None:
    from transformers import AttentionInterface

    AttentionInterface.register(ATTN_IMPLEMENTATION, tree_flash_attention_forward)


register_tree_flash_attention()