from __future__ import annotations

import base64
from io import BytesIO
from math import erf, sqrt

import numpy as np


def load_npz_text(path) -> dict[str, np.ndarray]:
    encoded = open(path, "rt", encoding="ascii").read()
    with np.load(BytesIO(base64.b64decode(encoded))) as archive:
        return {name: archive[name] for name in archive.files}


def linear(x: np.ndarray, weight: np.ndarray, bias: np.ndarray) -> np.ndarray:
    return x @ weight.T + bias


def layer_norm(
    x: np.ndarray,
    weight: np.ndarray,
    bias: np.ndarray,
    eps: float = 1e-5,
) -> np.ndarray:
    mean = x.mean(axis=-1, keepdims=True)
    variance = ((x - mean) ** 2).mean(axis=-1, keepdims=True)
    normalized = (x - mean) / np.sqrt(variance + eps)
    return normalized * weight + bias


def gelu(x: np.ndarray) -> np.ndarray:
    erf_values = np.vectorize(erf, otypes=[np.float32])(x / sqrt(2.0))
    return 0.5 * x * (1.0 + erf_values)


class NumpyStableGateDANN:
    """Inference-only StableGate-Hybrid DANN disease-classification branch."""

    def __init__(self, weights_path):
        self.weights = load_npz_text(weights_path)

    def predict_logits(self, expression: np.ndarray) -> np.ndarray:
        weights = self.weights
        x = np.asarray(expression, dtype=np.float32) * weights["gate_mask"]

        x = linear(x, weights["input_projection.0.weight"], weights["input_projection.0.bias"])
        x = layer_norm(x, weights["input_projection.1.weight"], weights["input_projection.1.bias"])
        x = gelu(x)

        block_input = x
        x = layer_norm(x, weights["encoder.0.block.0.weight"], weights["encoder.0.block.0.bias"])
        x = linear(x, weights["encoder.0.block.1.weight"], weights["encoder.0.block.1.bias"])
        x = gelu(x)
        x = linear(x, weights["encoder.0.block.4.weight"], weights["encoder.0.block.4.bias"])
        x = block_input + x

        x = layer_norm(x, weights["projection_head.0.weight"], weights["projection_head.0.bias"])
        x = linear(x, weights["projection_head.1.weight"], weights["projection_head.1.bias"])
        x = gelu(x)
        x = linear(x, weights["projection_head.4.weight"], weights["projection_head.4.bias"])
        x = layer_norm(x, weights["projection_head.5.weight"], weights["projection_head.5.bias"])

        x = linear(x, weights["disease_classifier.0.weight"], weights["disease_classifier.0.bias"])
        x = gelu(x)
        return linear(x, weights["disease_classifier.3.weight"], weights["disease_classifier.3.bias"])