# Copyright 2021 Amazon.com, Inc. or its affiliates. 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.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file 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.
from typing import Iterable, List, Optional
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
import logging
from numpy.random import RandomState

from syne_tune.optimizer.schedulers.searchers.bayesopt.tuning_algorithms.base_classes import (
    SurrogateModel,
    ScoringFunction,
    LocalOptimizer,
    SurrogateOutputModel,
    AcquisitionClassAndArgs,
    unwrap_acquisition_class_and_kwargs,
)
from syne_tune.optimizer.schedulers.searchers.bayesopt.datatypes.common import (
    Configuration,
)
from syne_tune.optimizer.schedulers.searchers.bayesopt.datatypes.hp_ranges import (
    HyperparameterRanges,
)

logger = logging.getLogger(__name__)


class IndependentThompsonSampling(ScoringFunction):
    """
    Note: This is not Thompson sampling, but rather a variant called
    "independent Thompson sampling", where means and variances are drawn
    from the marginal rather than the joint distribution. This is cheap,
    but incorrect.

    """

    def __init__(
        self, model: SurrogateModel, random_state: Optional[RandomState] = None
    ):
        self.model = model
        if random_state is None:
            random_state = RandomState(31415629)
        self.random_state = random_state

    def score(
        self,
        candidates: Iterable[Configuration],
        model: Optional[SurrogateModel] = None,
    ) -> List[float]:
        if model is None:
            model = self.model
        predictions_list = model.predict_candidates(candidates)
        scores = []
        # If the model supports fantasizing, posterior_means is a matrix. In
        # that case, samples are drawn for every column, then averaged (why
        # we need np.mean)
        for predictions in predictions_list:
            posterior_means = predictions["mean"]
            posterior_stds = predictions["std"]
            new_score = [
                np.mean(self.random_state.normal(m, s))
                for m, s in zip(posterior_means, posterior_stds)
            ]
            scores.append(new_score)
        return list(np.mean(np.array(scores), axis=0))


class LBFGSOptimizeAcquisition(LocalOptimizer):
    def __init__(
        self,
        hp_ranges: HyperparameterRanges,
        model: SurrogateOutputModel,
        acquisition_class: AcquisitionClassAndArgs,
        active_metric: str = None,
    ):
        super().__init__(hp_ranges, model, acquisition_class, active_metric)
        # Number criterion evaluations in last recent optimize call
        self.num_evaluations = None

    def optimize(
        self, candidate: Configuration, model: Optional[SurrogateOutputModel] = None
    ) -> Configuration:
        # Before local minimization, the model for this state_id should have been fitted.
        if model is None:
            model = self.model
        acquisition_class, acquisition_kwargs = unwrap_acquisition_class_and_kwargs(
            self.acquisition_class
        )
        acquisition_function = acquisition_class(
            model, self.active_metric, **acquisition_kwargs
        )

        x0 = self.hp_ranges.to_ndarray(candidate)
        bounds = self.hp_ranges.get_ndarray_bounds()
        n_evaluations = [0]  # wrapped in list to allow access from function

        # unwrap 2d arrays
        def f_df(x):
            n_evaluations[0] += 1
            return acquisition_function.compute_acq_with_gradient(x)

        res = fmin_l_bfgs_b(f_df, x0=x0, bounds=bounds, maxiter=1000)
        self.num_evaluations = n_evaluations[0]
        if res[2]["task"] == b"ABNORMAL_TERMINATION_IN_LNSRCH":
            # this condition was copied from the old GPyOpt code
            logger.warning(
                f"ABNORMAL_TERMINATION_IN_LNSRCH in lbfgs after {n_evaluations[0]} evaluations, "
                "returning original candidate"
            )
            return candidate  # returning original candidate
        else:
            # Clip to avoid situation where result is small epsilon out of bounds
            a_min, a_max = zip(*bounds)
            optimized_x = np.clip(res[0], a_min, a_max)
            # Make sure the above clipping does really just fix numerical rounding issues in LBFGS
            # if any bigger change was made there is a bug and we want to throw an exception
            assert np.linalg.norm(res[0] - optimized_x) < 1e-6, (
                res[0],
                optimized_x,
                bounds,
            )
            result = self.hp_ranges.from_ndarray(optimized_x.flatten())
            return result


class NoOptimization(LocalOptimizer):
    def optimize(
        self, candidate: Configuration, model: Optional[SurrogateModel] = None
    ) -> Configuration:
        return candidate

# Copyright 2021 Amazon.com, Inc. or its affiliates. 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.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file 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.
from typing import Iterable, List, Optional
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
import logging
from numpy.random import RandomState

from syne_tune.optimizer.schedulers.searchers.bayesopt.tuning_algorithms.base_classes import (
    SurrogateModel,
    ScoringFunction,
    LocalOptimizer,
    SurrogateOutputModel,
    AcquisitionClassAndArgs,
    unwrap_acquisition_class_and_kwargs,
)
from syne_tune.optimizer.schedulers.searchers.bayesopt.datatypes.common import (
    Configuration,
)
from syne_tune.optimizer.schedulers.searchers.bayesopt.datatypes.hp_ranges import (
    HyperparameterRanges,
)

logger = logging.getLogger(__name__)


class IndependentThompsonSampling(ScoringFunction):
    """
    Note: This is not Thompson sampling, but rather a variant called
    "independent Thompson sampling", where means and variances are drawn
    from the marginal rather than the joint distribution. This is cheap,
    but incorrect.

    """

    def __init__(
        self, model: SurrogateModel, random_state: Optional[RandomState] = None
    ):
        self.model = model
        if random_state is None:
            random_state = RandomState(31415629)
        self.random_state = random_state

    def score(
        self,
        candidates: Iterable[Configuration],
        model: Optional[SurrogateModel] = None,
    ) -> List[float]:
        if model is None:
            model = self.model
        predictions_list = model.predict_candidates(candidates)
        scores = []
        # If the model supports fantasizing, posterior_means is a matrix. In
        # that case, samples are drawn for every column, then averaged (why
        # we need np.mean)
        for predictions in predictions_list:
            posterior_means = predictions["mean"]
            posterior_stds = predictions["std"]
            new_score = [
                np.mean(self.random_state.normal(m, s))
                for m, s in zip(posterior_means, posterior_stds)
            ]
            scores.append(new_score)
        return list(np.mean(np.array(scores), axis=0))


class LBFGSOptimizeAcquisition(LocalOptimizer):
    def __init__(
        self,
        hp_ranges: HyperparameterRanges,
        model: SurrogateOutputModel,
        acquisition_class: AcquisitionClassAndArgs,
        active_metric: str = None,
    ):
        super().__init__(hp_ranges, model, acquisition_class, active_metric)
        # Number criterion evaluations in last recent optimize call
        self.num_evaluations = None

    def optimize(
        self, candidate: Configuration, model: Optional[SurrogateOutputModel] = None
    ) -> Configuration:
        # Before local minimization, the model for this state_id should have been fitted.
        if model is None:
            model = self.model
        acquisition_class, acquisition_kwargs = unwrap_acquisition_class_and_kwargs(
            self.acquisition_class
        )
        acquisition_function = acquisition_class(
            model, self.active_metric, **acquisition_kwargs
        )

        x0 = self.hp_ranges.to_ndarray(candidate)
        bounds = self.hp_ranges.get_ndarray_bounds()
        n_evaluations = [0]  # wrapped in list to allow access from function

        # unwrap 2d arrays
        def f_df(x):
            n_evaluations[0] += 1
            return acquisition_function.compute_acq_with_gradient(x)

        res = fmin_l_bfgs_b(f_df, x0=x0, bounds=bounds, maxiter=1000)
        self.num_evaluations = n_evaluations[0]
        if res[2]["task"] == b"ABNORMAL_TERMINATION_IN_LNSRCH":
            # this condition was copied from the old GPyOpt code
            logger.warning(
                f"ABNORMAL_TERMINATION_IN_LNSRCH in lbfgs after {n_evaluations[0]} evaluations, "
                "returning original candidate"
            )
            return candidate  # returning original candidate
        else:
            # Clip to avoid situation where result is small epsilon out of bounds
            a_min, a_max = zip(*bounds)
            optimized_x = np.clip(res[0], a_min, a_max)
            # Make sure the above clipping does really just fix numerical rounding issues in LBFGS
            # if any bigger change was made there is a bug and we want to throw an exception
            assert np.linalg.norm(res[0] - optimized_x) < 1e-6, (
                res[0],
                optimized_x,
                bounds,
            )
            result = self.hp_ranges.from_ndarray(optimized_x.flatten())
            return result


class NoOptimization(LocalOptimizer):
    def optimize(
        self, candidate: Configuration, model: Optional[SurrogateModel] = None
    ) -> Configuration:
        return candidate

# Copyright 2021 Amazon.com, Inc. or its affiliates. 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. # A copy of the License is located at # #     http://www.apache.org/licenses/LICENSE-2.0 # # or in the "license" file accompanying this file. This file 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. Note: This is not Thompson sampling, but rather a variant called     "independent Thompson sampling", where means and variances are drawn     from the marginal rather than the joint distribution. This is cheap,     but incorrect. # If the model supports fantasizing, posterior_means is a matrix. In # that case, samples are drawn for every column, then averaged (why # we need np.mean) # Number criterion evaluations in last recent optimize call # Before local minimization, the model for this state_id should have been fitted. # wrapped in list to allow access from function # unwrap 2d arrays # this condition was copied from the old GPyOpt code # returning original candidate # Clip to avoid situation where result is small epsilon out of bounds # Make sure the above clipping does really just fix numerical rounding issues in LBFGS # if any bigger change was made there is a bug and we want to throw an exception