"""Bidirectional MLX encoder for a thin EmbeddingGemma (true Metal inference).

mlx_lm's Gemma3 model runs CAUSALLY and reassigns sliding/full roles by layer
INDEX (ignoring our sliced layer_types) -> wrong embeddings (parity ~0.75-0.92).
EmbeddingGemma is BIDIRECTIONAL. This module reuses mlx_lm's Gemma3 layers but:
  * builds BIDIRECTIONAL masks (padding-only for full layers; symmetric sliding
    window for sliding layers),
  * sets the correct rope base per kept layer (sliding=10000, full=1e6) from
    config.layer_types,
then mean-pools. Result matches torch (parity ~1.0).
"""
import os, json
import numpy as np
import mlx.core as mx
from mlx_lm import load as mlx_load
from mlx_lm.models.rope_utils import initialize_rope

NEG = -1e9


def load_encoder(mlx_dir):
    """Load the converted mlx model and fix per-layer rope to match kept roles."""
    model, _ = mlx_load(mlx_dir)
    cfg = json.load(open(os.path.join(mlx_dir, "config.json")))
    layer_types = cfg["layer_types"]
    sw = int(cfg.get("sliding_window", 512))
    head_dim = int(cfg.get("head_dim", 256))
    hidden = int(cfg.get("hidden_size", 768))
    assert len(layer_types) == len(model.model.layers), "layer_types/layers mismatch"
    for role, blk in zip(layer_types, model.model.layers):
        base = 10000.0 if role == "sliding_attention" else 1_000_000.0
        blk.self_attn.rope = initialize_rope(dims=head_dim, base=base, traditional=False)
    model.eval()
    return model, layer_types, sw, hidden


def _masks(attn_mask, L, sw):
    """Additive bidirectional masks: (full=[B,1,1,L] padding, sliding=+window)."""
    pad = (1.0 - attn_mask)[:, None, None, :] * NEG          # [B,1,1,L]
    idx = mx.arange(L)
    within = mx.abs(idx[:, None] - idx[None, :]) < sw         # [L,L] bool, symmetric
    win = mx.where(within, 0.0, NEG)[None, None]              # [1,1,L,L]
    return pad, pad + win


def encode(model, layer_types, sw, hidden, input_ids, attn_mask):
    """input_ids/attn_mask: mx int arrays [B,L]. Returns L2-normalized mean-pooled embs [B,d]."""
    h = model.model.embed_tokens(input_ids)
    h = h * mx.array(hidden ** 0.5, mx.bfloat16).astype(h.dtype)
    B, L = input_ids.shape
    full_mask, slide_mask = _masks(attn_mask.astype(h.dtype), L, sw)
    for role, blk in zip(layer_types, model.model.layers):
        m = slide_mask if role == "sliding_attention" else full_mask
        h = blk(h, m.astype(h.dtype), None)
    h = model.model.norm(h)                                   # [B,L,d]
    am = attn_mask.astype(h.dtype)[:, :, None]
    v = (h * am).sum(1) / mx.maximum(am.sum(1), 1e-9)         # mean pool
    v = v / mx.linalg.norm(v, axis=-1, keepdims=True)
    return v


def embed_texts(model, layer_types, sw, hidden, tok, texts, max_len=256, batch=32):
    out = []
    for i in range(0, len(texts), batch):
        chunk = texts[i:i + batch]
        enc = tok(chunk, padding=True, truncation=True, max_length=max_len, return_tensors="np")
        ids = mx.array(enc["input_ids"].astype(np.int32))
        am = mx.array(enc["attention_mask"].astype(np.int32))
        v = encode(model, layer_types, sw, hidden, ids, am)
        mx.eval(v)
        out.append(np.array(v))
    return np.concatenate(out, 0)