import json
import logging
from typing import List, Optional

import numpy as np

from database import MotionDatabase


logger = logging.getLogger(__name__)


def compute_similarities(
    vector_type: str = "fused",
    window_id: Optional[str] = None,
    top_k: int = 10,
    db_path: Optional[str] = None,
):
    """Compute pairwise cosine similarities for vectors of a given type and store top-k neighbors.

    Returns number of inserted rows.
    """
    db = MotionDatabase(db_path=db_path) if db_path is not None else MotionDatabase()

    # Build SQL query depending on vector type
    if vector_type == "fused":
        if window_id is not None:
            query = "SELECT motion_id AS id, vector FROM fused_embeddings WHERE window_id = ?"
            params = (window_id,)
        else:
            # fallback to all fused embeddings if no window specified
            query = "SELECT motion_id AS id, vector FROM fused_embeddings"
            params = ()
    elif vector_type == "text":
        query = "SELECT motion_id AS id, vector FROM embeddings"
        params = ()
    elif vector_type == "svd":
        if window_id is not None:
            query = "SELECT entity_id AS id, vector FROM svd_vectors WHERE entity_type = 'motion' AND window_id = ?"
            params = (window_id,)
        else:
            query = "SELECT entity_id AS id, vector FROM svd_vectors WHERE entity_type = 'motion'"
            params = ()
    else:
        logger.error(f"Unknown vector_type: {vector_type}")
        return 0

    # Load vectors in a single query
    try:
        try:
            import duckdb
        except Exception:
            logger.exception("duckdb import failed; cannot load vectors")
            return 0

        with duckdb.connect(db.db_path) as conn:
            rows = conn.execute(query, params).fetchall()
    except Exception:
        logger.exception("Error loading vectors for similarity compute")
        return 0

    if not rows:
        logger.info("No vectors found for %s window=%s", vector_type, window_id)
        return 0

    ids: List[int] = []
    vecs: List[List[float]] = []

    for r in rows:
        _id, vec_json = r
        # parse vector robustly: accept list/tuple, bytes/bytearray, or JSON string
        vec = None
        if isinstance(vec_json, (list, tuple)):
            vec = list(vec_json)
        elif isinstance(vec_json, (bytes, bytearray)):
            try:
                text = vec_json.decode("utf-8")
            except Exception:
                logger.warning(
                    "Skipping row with non-decodable bytes vector for id=%s", _id
                )
                continue
            try:
                vec = json.loads(text)
            except Exception:
                logger.warning(
                    "Skipping row with invalid JSON bytes vector for id=%s", _id
                )
                continue
        elif isinstance(vec_json, str):
            try:
                vec = json.loads(vec_json)
            except Exception:
                logger.warning(
                    "Skipping row with invalid JSON string vector for id=%s", _id
                )
                continue
        else:
            logger.warning(
                "Skipping row with unsupported vector type %s for id=%s",
                type(vec_json),
                _id,
            )
            continue

        # ensure numeric conversion
        try:
            vec_floats = [float(x) for x in vec]
        except Exception:
            logger.warning(
                "Skipping row with non-numeric vector entries for id=%s", _id
            )
            continue

        # cast id to int for consistency; skip if cannot cast
        try:
            ids.append(int(_id))
        except Exception:
            logger.warning("Skipping row with non-integer id=%s", _id)
            continue

        vecs.append(vec_floats)

    if not vecs:
        logger.info(
            "No valid vectors after parsing for %s window=%s", vector_type, window_id
        )
        return 0

    # Ensure consistent dimensionality: pad shorter vectors with zeros
    lengths = [len(v) for v in vecs]
    max_dim = max(lengths)
    if len(set(lengths)) != 1:
        logger.warning(
            "Inconsistent vector dimensions detected (max=%d). Padding shorter vectors with zeros.",
            max_dim,
        )

    matrix = np.zeros((len(vecs), max_dim), dtype=np.float32)
    for i, v in enumerate(vecs):
        matrix[i, : len(v)] = v

    # Normalize rows
    norms = np.linalg.norm(matrix, axis=1, keepdims=True)
    # avoid division by zero
    norms[norms == 0] = 1.0
    normalized = matrix / norms

    # Compute similarity matrix
    sim = normalized @ normalized.T

    n = sim.shape[0]
    rows_to_insert: List[dict] = []

    for i in range(n):
        scores = sim[i].copy()
        # exclude self
        scores[i] = -np.inf

        # number of neighbors to take is min(top_k, n-1)
        k = min(top_k, n - 1)
        if k <= 0:
            continue

        # get top k indices
        if k == 1:
            idx = int(np.argmax(scores))
            top_idx = [idx]
        else:
            # argpartition for efficiency then sort. avoid negating scores because
            # we set self to -inf earlier which would become +inf when negated and
            # incorrectly be picked as a top neighbor. Instead, partition at
            # n - k to obtain the k largest elements, then sort them descending.
            part = np.argpartition(scores, n - k)[-k:]
            top_idx = part[np.argsort(-scores[part])]

        src_id = ids[i]
        for j in top_idx:
            rows_to_insert.append(
                {
                    "source_motion_id": int(src_id),
                    "target_motion_id": int(ids[j]),
                    "score": float(scores[j]),
                    "vector_type": vector_type,
                    "window_id": window_id,
                }
            )

    # Clear existing cache for this vector_type/window and store new rows
    try:
        deleted = db.clear_similarity_cache(
            vector_type=vector_type, window_id=window_id
        )
        logger.info(
            "Cleared %d existing similarity rows for %s window=%s",
            deleted,
            vector_type,
            window_id,
        )
    except Exception:
        logger.exception("Error clearing similarity cache")

    try:
        inserted = db.store_similarity_batch(rows_to_insert)
        logger.info(
            "Inserted %d similarity rows for %s window=%s",
            inserted,
            vector_type,
            window_id,
        )
        return inserted
    except Exception:
        logger.exception("Error storing similarity rows")
        return 0