"""CSA indexer — Python API bridge.

Wraps the CUDA indexer score+topk kernel with the interface that
AttentionSubBlock expects.

The indexer (paper §2.3.5, eq. 16) scores each query against
compressed blocks via weighted ReLU MQA logits, then selects
top-k blocks for sparse attention.

Currently uses scalar FP32 CUDA cores after FP4 dequant.
The FP4 tensor-core path (Stage F / E7) is a future optimization.
"""
import torch
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from dsv4.cache.handle import LayerCacheHandle


def compute_index_scores_topk(
    q_indexer: torch.Tensor,       # (T, n_I_h * c_I) BF16 — indexer query
    w_indexer: torch.Tensor,       # (T, n_I_h) FP32 — per-head weights
    cache: "LayerCacheHandle",     # provides FP4 indexer keys
    top_k: int = 512,              # number of blocks to select
) -> torch.Tensor:                 # (T, top_k) int64 — selected block indices
    """CSA: score compressed entries and select top-k blocks.

    Uses the CUDA indexer_score_topk kernel (raw CUDA, FP4 dequant + scalar
    score + min-heap top-k). Returns entry indices for gather_compressed_kv.
    """
    from dsv4.kernels.indexer.score_topk import run_indexer_score_topk

    # Read the indexer view from the cache
    indexer_view = cache.read_indexer_view()

    # c_I is the indexer head dimension from schema
    n_I_h = cache.schema.indexer_entries_per_block  # This is entries, not heads
    c_I = cache.schema.indexer_head_dim  # 128

    # n_I_h (number of indexer heads) comes from the config, not the schema.
    # We need to pass it through the handle or compute it.
    # For DSV4: n_I_h = 64 (same for Flash and Pro)
    # TODO: add indexer_num_heads to schema or handle
    n_I_h = 64  # config.indexer_num_heads, hardcoded for now

    # Reshape q_indexer from (T, n_I_h * c_I) to (T, n_I_h * c_I) — already flat
    # The kernel expects q_I: [T, n_I_h * c_I] BF16
    # and w_h: [T, n_I_h] FP32

    entries_per_block = cache.schema.entries_per_block

    indices = run_indexer_score_topk(
        q_I=q_indexer,
        w_h=w_indexer.float() if w_indexer.dtype != torch.float32 else w_indexer,
        indexer_view=indexer_view,
        num_heads=n_I_h,
        head_dim=c_I,
        top_k=top_k,
        entries_per_block=entries_per_block,
    )

    # indices: (T, top_k) int32 → convert to int64 for gather_compressed_kv
    return indices.to(torch.int64)