# Copyright 2024 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
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"""Constructors for MLPs."""

import haiku as hk
import jax
import jax.numpy as jnp


# TODO(aelkadi): Move the mlp factory here from `deep_typed_graph_net.py`.


class LinearNormConditioning(hk.Module):
  """Module for norm conditioning.

  Conditions the normalization of "inputs" by applying a linear layer to the
  "norm_conditioning" which produces the scale and variance which are applied to
  each channel (across the last dim) of "inputs".
  """

  def __init__(self, name="norm_conditioning"):
    super().__init__(name=name)

  def __call__(self, inputs: jax.Array, norm_conditioning: jax.Array):

    feature_size = inputs.shape[-1]
    conditional_linear_layer = hk.Linear(
        output_size=2 * feature_size,
        w_init=hk.initializers.TruncatedNormal(stddev=1e-8),
    )
    conditional_scale_offset = conditional_linear_layer(norm_conditioning)
    scale_minus_one, offset = jnp.split(conditional_scale_offset, 2, axis=-1)
    scale = scale_minus_one + 1.
    return inputs * scale + offset