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# Copyright 2022 DeepMind Technologies Limited. All Rights Reserved.
#
# 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.
# ==============================================================================
"""RASP Evaluator which applies causal masks to selectors."""
from typing import Sequence, Union
import numpy as np
from tracr.rasp import rasp
class CausalEvaluator(rasp.DefaultRASPEvaluator):
"""Evaluates RASP with causal masking."""
def evaluate(
self, expr: rasp.RASPExpr, xs: Sequence[rasp.Value]
) -> Union[Sequence[rasp.Value], rasp.SelectorValue]:
out = super().evaluate(expr, xs)
if not isinstance(expr, rasp.Selector):
return out
out = np.array(out)
causal_mask = np.tril(np.full(out.shape, 1))
return np.logical_and(causal_mask, out).tolist()
evaluate = CausalEvaluator().evaluate
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