"""GPU-accelerated attention mechanisms for multi-head consensus.

Provides attention-based consensus scoring for the Dvādaśa pipeline:
- Head output attention: weight head contributions by relevance
- Claim-level attention: cross-attend between claims for conflict detection
- Weighted consensus: attention-based aggregation of head outputs
"""

from __future__ import annotations

from typing import Any

from fusionagi._logger import logger
from fusionagi.gpu.backend import TensorBackend, get_backend


def attention_consensus(
    head_embeddings: list[list[str]],
    query_text: str,
    head_weights: list[float] | None = None,
    num_heads: int = 4,
    backend: TensorBackend | None = None,
) -> dict[str, Any]:
    """Score head contributions using multi-head attention against the query.

    Each head's claims are embedded, then cross-attended against the query
    to produce relevance-weighted scores.

    Args:
        head_embeddings: List of claim-text lists, one per head.
        query_text: The user's original query.
        head_weights: Optional per-head reliability weights.
        num_heads: Number of attention heads.
        backend: TensorBackend to use.

    Returns:
        Dict with 'head_scores' (list of floats), 'attention_weights' (matrix),
        and 'consensus_score' (float).
    """
    be = backend or get_backend()
    import numpy as np

    if not head_embeddings:
        return {"head_scores": [], "attention_weights": [], "consensus_score": 0.0}

    all_claims: list[str] = []
    head_indices: list[int] = []
    for i, claims in enumerate(head_embeddings):
        for claim in claims:
            all_claims.append(claim)
            head_indices.append(i)

    if not all_claims:
        return {
            "head_scores": [0.0] * len(head_embeddings),
            "attention_weights": [],
            "consensus_score": 0.0,
        }

    query_emb = be.embed_texts([query_text])
    claim_emb = be.embed_texts(all_claims)

    query_np = be.to_numpy(query_emb)
    claims_np = be.to_numpy(claim_emb)

    query_expanded = np.tile(query_np, (len(all_claims), 1))
    attn_output = be.to_numpy(
        be.multi_head_attention(
            be.from_numpy(query_expanded),
            be.from_numpy(claims_np),
            be.from_numpy(claims_np),
            num_heads=num_heads,
        )
    )

    relevance = np.sum(attn_output * claims_np, axis=1)

    num_heads_count = len(head_embeddings)
    head_scores = np.zeros(num_heads_count, dtype=np.float32)
    head_claim_counts = np.zeros(num_heads_count, dtype=np.float32)

    for idx, head_idx in enumerate(head_indices):
        head_scores[head_idx] += relevance[idx]
        head_claim_counts[head_idx] += 1.0

    safe_counts: Any = np.maximum(head_claim_counts, 1.0)
    head_scores = head_scores / safe_counts

    if head_weights is not None:
        w = np.array(head_weights[:num_heads_count], dtype=np.float32)
        head_scores = head_scores * w

    score_min = head_scores.min() if len(head_scores) > 0 else 0.0
    score_max = head_scores.max() if len(head_scores) > 0 else 1.0
    score_range = score_max - score_min
    if score_range > 0:
        head_scores_norm = (head_scores - score_min) / score_range
    else:
        head_scores_norm = np.ones_like(head_scores) * 0.5

    consensus_score = float(np.mean(head_scores_norm)) if len(head_scores_norm) > 0 else 0.0

    logger.debug(
        "Attention consensus computed",
        extra={
            "num_heads": num_heads_count,
            "total_claims": len(all_claims),
            "consensus_score": consensus_score,
        },
    )

    return {
        "head_scores": head_scores_norm.tolist(),
        "attention_weights": relevance.tolist(),
        "consensus_score": consensus_score,
    }


def cross_claim_attention(
    claims: list[str],
    num_heads: int = 4,
    backend: TensorBackend | None = None,
) -> dict[str, Any]:
    """Cross-attend between claims to detect agreement and conflict.

    Args:
        claims: List of claim texts.
        num_heads: Number of attention heads.
        backend: TensorBackend to use.

    Returns:
        Dict with 'similarity_matrix' and 'conflict_pairs' (indices).
    """
    be = backend or get_backend()

    if len(claims) < 2:
        return {"similarity_matrix": [], "conflict_pairs": []}

    embeddings = be.embed_texts(claims)
    emb_np = be.to_numpy(embeddings)

    attn_out = be.to_numpy(
        be.multi_head_attention(
            be.from_numpy(emb_np),
            be.from_numpy(emb_np),
            be.from_numpy(emb_np),
            num_heads=num_heads,
        )
    )

    sim = be.to_numpy(be.cosine_similarity_matrix(be.from_numpy(attn_out), be.from_numpy(attn_out)))

    conflict_pairs: list[tuple[int, int]] = []
    for i in range(len(claims)):
        for j in range(i + 1, len(claims)):
            if sim[i, j] < 0.3:
                conflict_pairs.append((i, j))

    return {
        "similarity_matrix": sim.tolist(),
        "conflict_pairs": conflict_pairs,
    }