"""Small public wrapper around the bundled Basenji Saluki implementation."""

from __future__ import annotations

import json
import os
from pathlib import Path
from typing import Any, Mapping


def load_params(path: str | Path) -> dict[str, Any]:
    """Load parameters and normalize historical scalar multi-head settings."""

    with Path(path).open(encoding="utf-8") as handle:
        params = json.load(handle)

    model_params = dict(params["model"])
    num_targets = model_params.get("num_targets", 1)
    heads = int(model_params.get("heads", 1))
    if not isinstance(num_targets, list):
        model_params["num_targets"] = [num_targets] * heads

    normalized = dict(params)
    normalized["model"] = model_params
    return normalized


class SalukiModel:
    """Construct, restore and run one head of the official Saluki network."""

    def __init__(self, params: Mapping[str, Any], head: int = 0):
        os.environ.setdefault("TF_USE_LEGACY_KERAS", "1")
        from .basenji.rnann import RnaNN

        model_params = dict(params["model"] if "model" in params else params)
        num_targets = model_params.get("num_targets", 1)
        heads = int(model_params.get("heads", 1))
        if not isinstance(num_targets, list):
            model_params["num_targets"] = [num_targets] * heads

        self.network = RnaNN(model_params)
        if not 0 <= head < len(self.network.models):
            raise ValueError(
                f"head must be in [0, {len(self.network.models) - 1}], got {head}"
            )
        self.head = head
        self.network.model = self.network.models[head]

    @property
    def keras_model(self):
        return self.network.models[self.head]

    def restore(self, weight_path: str | Path) -> "SalukiModel":
        self.network.restore(str(weight_path), head_i=self.head)
        return self

    def predict(self, inputs, **kwargs):
        return self.keras_model.predict(inputs, **kwargs)