#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
#  Released under the modified BSD license. See COPYING.md for more details.

import logging
from typing import Dict, List, TYPE_CHECKING, Tuple, Any, Optional, Set

import numpy as np
from overrides import overrides

from miplearn.classifiers import Classifier
from miplearn.classifiers.counting import CountingClassifier
from miplearn.classifiers.threshold import MinProbabilityThreshold, Threshold
from miplearn.components.component import Component
from miplearn.components.dynamic_common import DynamicConstraintsComponent
from miplearn.features.sample import Sample
from miplearn.instance.base import Instance
from miplearn.types import LearningSolveStats

logger = logging.getLogger(__name__)

if TYPE_CHECKING:
    from miplearn.solvers.learning import LearningSolver


class DynamicLazyConstraintsComponent(Component):
    """
    A component that predicts which lazy constraints to enforce.
    """

    def __init__(
        self,
        classifier: Classifier = CountingClassifier(),
        threshold: Threshold = MinProbabilityThreshold([0, 0.05]),
    ):
        self.dynamic: DynamicConstraintsComponent = DynamicConstraintsComponent(
            classifier=classifier,
            threshold=threshold,
            attr="lazy_enforced",
        )
        self.classifiers = self.dynamic.classifiers
        self.thresholds = self.dynamic.thresholds
        self.known_cids = self.dynamic.known_cids
        self.lazy_enforced: Set[str] = set()

    @staticmethod
    def enforce(
        cids: List[str],
        instance: Instance,
        model: Any,
        solver: "LearningSolver",
    ) -> None:
        assert solver.internal_solver is not None
        for cid in cids:
            instance.enforce_lazy_constraint(solver.internal_solver, model, cid)

    @overrides
    def before_solve_mip(
        self,
        solver: "LearningSolver",
        instance: Instance,
        model: Any,
        stats: LearningSolveStats,
        sample: Sample,
    ) -> None:
        self.lazy_enforced.clear()
        logger.info("Predicting violated (dynamic) lazy constraints...")
        cids = self.dynamic.sample_predict(instance, sample)
        logger.info("Enforcing %d lazy constraints..." % len(cids))
        self.enforce(cids, instance, model, solver)

    @overrides
    def after_solve_mip(
        self,
        solver: "LearningSolver",
        instance: Instance,
        model: Any,
        stats: LearningSolveStats,
        sample: Sample,
    ) -> None:
        sample.put("lazy_enforced", set(self.lazy_enforced))

    @overrides
    def iteration_cb(
        self,
        solver: "LearningSolver",
        instance: Instance,
        model: Any,
    ) -> bool:
        assert solver.internal_solver is not None
        logger.debug("Finding violated lazy constraints...")
        cids = instance.find_violated_lazy_constraints(solver.internal_solver, model)
        if len(cids) == 0:
            logger.debug("No violations found")
            return False
        else:
            self.lazy_enforced |= set(cids)
            logger.debug("    %d violations found" % len(cids))
            self.enforce(cids, instance, model, solver)
            return True

    # Delegate ML methods to self.dynamic
    # -------------------------------------------------------------------
    @overrides
    def sample_xy(
        self,
        instance: Optional[Instance],
        sample: Sample,
    ) -> Tuple[Dict, Dict]:
        return self.dynamic.sample_xy(instance, sample)

    @overrides
    def pre_fit(self, pre: List[Any]) -> None:
        self.dynamic.pre_fit(pre)

    def sample_predict(
        self,
        instance: Instance,
        sample: Sample,
    ) -> List[str]:
        return self.dynamic.sample_predict(instance, sample)

    @overrides
    def pre_sample_xy(self, instance: Instance, sample: Sample) -> Any:
        return self.dynamic.pre_sample_xy(instance, sample)

    @overrides
    def fit_xy(
        self,
        x: Dict[str, np.ndarray],
        y: Dict[str, np.ndarray],
    ) -> None:
        self.dynamic.fit_xy(x, y)

    @overrides
    def sample_evaluate(
        self,
        instance: Instance,
        sample: Sample,
    ) -> Dict[str, Dict[str, float]]:
        return self.dynamic.sample_evaluate(instance, sample)