# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Base class for QLKNN Models."""
import abc
from typing import TypeAlias
import jax
from torax.transport_model import qualikiz_based_transport_model
ModelOutput: TypeAlias = dict[str, jax.Array]
InputsAndRanges: TypeAlias = dict[str, dict[str, float]]
[docs]
class BaseQLKNNModel(abc.ABC):
"""Base class for QLKNN Models."""
def __init__(self, path: str, name: str):
self.path = path
self.name = name
@property
@abc.abstractmethod
def inputs_and_ranges(self) -> InputsAndRanges:
raise NotImplementedError()
@abc.abstractmethod
def predict(
self,
inputs: jax.Array,
) -> ModelOutput:
raise NotImplementedError()
@abc.abstractmethod
def get_model_inputs_from_qualikiz_inputs(
self, qualikiz_inputs: qualikiz_based_transport_model.QualikizInputs
) -> jax.Array:
raise NotImplementedError()