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+# Copyright (c) 2023 Cisco and/or its affiliates.
+# 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.
+
+"""Module defining StrategyBase class."""
+
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Callable, Optional, Tuple
+
+from ..discrete_interval import DiscreteInterval
+from ..discrete_load import DiscreteLoad
+from ..discrete_width import DiscreteWidth
+from ..expander import TargetedExpander
+from ..limit_handler import LimitHandler
+from ..relevant_bounds import RelevantBounds
+from ..target_spec import TargetSpec
+
+
+@dataclass
+class StrategyBase(ABC):
+ """Abstract class encompassing data common to most strategies.
+
+ A strategy is one piece of logic a selector may use
+ when nominating a candidate according to its current target.
+
+ The two initial bound arguments may not be bounds at all.
+ For initial targets, the two values are usually mrr and mrr2.
+ For subsequent targets, the initial values are usually
+ the relevant bounds of the preceding target,
+ but one of them may be None if hitting min or max load.
+
+ The initial values are mainly used as stable alternatives
+ to relevant bounds of preceding target,
+ because those bounds may have been unpredictably altered
+ by nominations from unrelated search goals.
+ This greatly simplifies reasoning about strategies making progress.
+ """
+
+ target: TargetSpec
+ """The target this strategy is focusing on."""
+ expander: TargetedExpander
+ """Instance to track width expansion during search (if applicable)."""
+ initial_lower_load: Optional[DiscreteLoad]
+ """Smaller of the two loads distinguished at instance creation.
+ Can be None if upper bound is the min load."""
+ initial_upper_load: Optional[DiscreteLoad]
+ """Larger of the two loads distinguished at instance creation.
+ Can be None if lower bound is the max load."""
+ handler: LimitHandler = field(repr=False)
+ """Reference to the limit handler instance."""
+ debug: Callable[[str], None] = field(repr=False)
+ """Injectable function for debug logging."""
+
+ @abstractmethod
+ def nominate(
+ self, bounds: RelevantBounds
+ ) -> Tuple[Optional[DiscreteLoad], Optional[DiscreteWidth]]:
+ """Nominate a load candidate if the conditions activate this strategy.
+
+ A complete candidate refers also to the nominating selector.
+ To prevent circular dependence (selector refers to nominating strategy),
+ this function returns only duration and width.
+
+ Width should only be non-None if global current width should be updated
+ when the candidate based on this becomes winner.
+ But currently all strategies return non-None width
+ if they return non-None load.
+
+ :param bounds: Freshly updated bounds relevant for current target.
+ :type bounds: RelevantBounds
+ :returns: Two nones or candidate intended load and duration.
+ :rtype: Tuple[Optional[DiscreteLoad], Optional[DiscreteWidth]]
+ """
+ return None, None
+
+ def won(self, bounds: RelevantBounds, load: DiscreteLoad) -> None:
+ """Notify the strategy its candidate became the winner.
+
+ Most strategies have no use for this information,
+ but some strategies may need to update their private information.
+
+ :param bounds: Freshly updated bounds relevant for current target.
+ :param load: The current load, so strategy does not need to remember.
+ :type bounds: RelevantBounds
+ :type load: DiscreteLoad
+ """
+ return
+
+ def not_worth(self, bounds: RelevantBounds, load: DiscreteLoad) -> bool:
+ """A check on bounds common for multiple strategies.
+
+ The load is worth measuring only if it can create or improve
+ either relevant bound.
+
+ Each strategy is designed to create a relevant bound for current target,
+ which is only needed if that (or better) bound does not exist yet.
+ Conversely, if a strategy does not nominate, it is because
+ the load it would nominate (if any) is found not worth by this method.
+
+ :param bounds: Current relevant bounds.
+ :param load: Load of a possible candidate.
+ :type bounds: RelevantBounds
+ :type load: DiscreteLoad
+ :returns: True if the load should NOT be nominated.
+ :rtype: bool
+ """
+ if bounds.clo and bounds.clo >= load:
+ return True
+ if bounds.chi and bounds.chi <= load:
+ return True
+ if bounds.clo and bounds.chi:
+ # We are not hitting min nor max load.
+ # Measuring at this load will create or improve clo or chi.
+ # The only reason not to nominate is if interval is narrow already.
+ wig = DiscreteInterval(
+ lower_bound=bounds.clo,
+ upper_bound=bounds.chi,
+ ).width_in_goals(self.target.discrete_width)
+ if wig <= 1.0:
+ return True
+ return False