import plotly.io as pio

# Ax uses Plotly to produce interactive plots. These are great for viewing and analysis,
# though they also lead to large file sizes, which is not ideal for files living in GH.
# Changing the default to `png` strips the interactive components to get around this.
pio.renderers.default = "png"

import math
from typing import Optional

from botorch.acquisition.monte_carlo import MCAcquisitionFunction
from botorch.models.model import Model
from botorch.sampling.base import MCSampler
from botorch.sampling.normal import SobolQMCNormalSampler
from botorch.utils import t_batch_mode_transform
from torch import Tensor


class qScalarizedUpperConfidenceBound(MCAcquisitionFunction):
    def __init__(
        self,
        model: Model,
        beta: Tensor,
        weights: Tensor,
        sampler: Optional[MCSampler] = None,
    ) -> None:
        # we use the AcquisitionFunction constructor, since that of
        # MCAcquisitionFunction performs some validity checks that we don't want here
        super(MCAcquisitionFunction, self).__init__(model=model)
        if sampler is None:
            sampler = SobolQMCNormalSampler(sample_shape=torch.Size([512]))
        self.sampler = sampler
        self.register_buffer("beta", torch.as_tensor(beta))
        self.register_buffer("weights", torch.as_tensor(weights))

    @t_batch_mode_transform()
    def forward(self, X: Tensor) -> Tensor:
        """Evaluate scalarized qUCB on the candidate set `X`.

        Args:
            X: A `(b) x q x d`-dim Tensor of `(b)` t-batches with `q` `d`-dim
                design points each.

        Returns:
            Tensor: A `(b)`-dim Tensor of Upper Confidence Bound values at the
                given design points `X`.
        """
        posterior = self.model.posterior(X)
        samples = self.get_posterior_samples(posterior)  # n x b x q x o
        scalarized_samples = samples.matmul(self.weights)  # n x b x q
        mean = posterior.mean  # b x q x o
        scalarized_mean = mean.matmul(self.weights)  # b x q
        ucb_samples = (
            scalarized_mean
            + math.sqrt(self.beta * math.pi / 2)
            * (scalarized_samples - scalarized_mean).abs()
        )
        return ucb_samples.max(dim=-1)[0].mean(dim=0)

import torch

from botorch.fit import fit_gpytorch_mll
from botorch.models import SingleTaskGP
from botorch.utils import standardize
from gpytorch.mlls import ExactMarginalLogLikelihood


# generate synthetic data
X = torch.rand(20, 2)
Y = torch.stack([torch.sin(X[:, 0]), torch.cos(X[:, 1])], -1)
Y = standardize(Y)  # standardize to zero mean unit variance

# construct and fit the multi-output model
gp = SingleTaskGP(X, Y)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
fit_gpytorch_mll(mll)

# construct the acquisition function
qSUCB = qScalarizedUpperConfidenceBound(gp, beta=0.1, weights=torch.tensor([0.1, 0.5]))

# evaluate on single q-batch with q=3
qSUCB(torch.rand(3, 2))

# batch-evaluate on two q-batches with q=3
qSUCB(torch.rand(2, 3, 2))

from botorch.acquisition import AnalyticAcquisitionFunction


class ScalarizedUpperConfidenceBound(AnalyticAcquisitionFunction):
    def __init__(
        self,
        model: Model,
        beta: Tensor,
        weights: Tensor,
        maximize: bool = True,
    ) -> None:
        # we use the AcquisitionFunction constructor, since that of
        # AnalyticAcquisitionFunction performs some validity checks that we don't want here
        super(AnalyticAcquisitionFunction, self).__init__(model)
        self.maximize = maximize
        self.register_buffer("beta", torch.as_tensor(beta))
        self.register_buffer("weights", torch.as_tensor(weights))

    @t_batch_mode_transform(expected_q=1)
    def forward(self, X: Tensor) -> Tensor:
        """Evaluate the Upper Confidence Bound on the candidate set X using scalarization

        Args:
            X: A `(b) x d`-dim Tensor of `(b)` t-batches of `d`-dim design
                points each.

        Returns:
            A `(b)`-dim Tensor of Upper Confidence Bound values at the given
                design points `X`.
        """
        self.beta = self.beta.to(X)
        batch_shape = X.shape[:-2]
        posterior = self.model.posterior(X)
        means = posterior.mean.squeeze(dim=-2)  # b x o
        scalarized_mean = means.matmul(self.weights)  # b
        covs = posterior.mvn.covariance_matrix  # b x o x o
        weights = self.weights.view(
            1, -1, 1
        )  # 1 x o x 1 (assume single batch dimension)
        weights = weights.expand(batch_shape + weights.shape[1:])  # b x o x 1
        weights_transpose = weights.permute(0, 2, 1)  # b x 1 x o
        scalarized_variance = torch.bmm(
            weights_transpose, torch.bmm(covs, weights)
        ).view(
            batch_shape
        )  # b
        delta = (self.beta.expand_as(scalarized_mean) * scalarized_variance).sqrt()
        if self.maximize:
            return scalarized_mean + delta
        else:
            return scalarized_mean - delta

# construct the acquisition function
SUCB = ScalarizedUpperConfidenceBound(gp, beta=0.1, weights=torch.tensor([0.1, 0.5]))

# evaluate on single point
SUCB(torch.rand(1, 2))

# batch-evaluate on 3 points
SUCB(torch.rand(3, 1, 2))

from typing import List
from typing import Any, Dict

from botorch.acquisition.input_constructors import acqf_input_constructor


@acqf_input_constructor(ScalarizedUpperConfidenceBound)
def construct_inputs_scalarized_ucb(
    model: Model,
    beta: float,
    weights: List[float],
    **kwargs: Any,
) -> Dict[str, Any]:
    return {
        "model": model,
        "beta": torch.as_tensor(beta, dtype=torch.double),
        "weights": torch.as_tensor(weights, dtype=torch.double),
    }

from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
from ax.modelbridge.registry import Models


gs = GenerationStrategy(
    steps=[
        # Quasi-random initialization step
        GenerationStep(
            model=Models.SOBOL,
            num_trials=5,  # How many trials should be produced from this generation step
            model_kwargs={"seed": 999},  # Any kwargs you want passed into the model
        ),
        # Bayesian optimization step using the custom acquisition function
        GenerationStep(
            model=Models.BOTORCH_MODULAR,
            num_trials=-1,  # No limitation on how many trials should be produced from this step
            # For `BOTORCH_MODULAR`, we pass in kwargs to specify what surrogate or acquisition function to use.
            # `acquisition_options` specifies the set of additional arguments to pass into the input constructor.
            model_kwargs={
                "botorch_acqf_class": ScalarizedUpperConfidenceBound,
                "acquisition_options": {"beta": 0.1, "weights": [1.0, 1.0]},
            },
        ),
    ]
)

from ax.service.ax_client import AxClient
from ax.service.utils.instantiation import ObjectiveProperties
from botorch.test_functions import BraninCurrin


# Initialize the client - AxClient offers a convenient API to control the experiment
ax_client = AxClient(generation_strategy=gs)
# Setup the experiment
ax_client.create_experiment(
    name="branincurrin_test_experiment",
    parameters=[
        {
            "name": f"x{i+1}",
            "type": "range",
            # It is crucial to use floats for the bounds, i.e., 0.0 rather than 0.
            # Otherwise, the parameter would
            "bounds": [0.0, 1.0],
        }
        for i in range(2)
    ],
    objectives={
        "branin": ObjectiveProperties(minimize=True),
        "currin": ObjectiveProperties(minimize=True),
    },
)
# Setup a function to evaluate the trials
branincurrin = BraninCurrin()


def evaluate(parameters):
    x = torch.tensor([[parameters.get(f"x{i+1}") for i in range(2)]])
    bc_eval = branincurrin(x).squeeze().tolist()
    # In our case, standard error is 0, since we are computing a synthetic function.
    return {"branin": (bc_eval[0], 0.0), "currin": (bc_eval[1], 0.0)}

for i in range(10):
    parameters, trial_index = ax_client.get_next_trial()
    # Local evaluation here can be replaced with deployment to external system.
    ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate(parameters))

ax_client.generation_strategy.trials_as_df

from ax.plot.pareto_frontier import plot_pareto_frontier
from ax.plot.pareto_utils import compute_posterior_pareto_frontier
from ax.utils.notebook.plotting import render


objectives = ax_client.experiment.optimization_config.objective.objectives
frontier = compute_posterior_pareto_frontier(
    experiment=ax_client.experiment,
    data=ax_client.experiment.fetch_data(),
    primary_objective=objectives[1].metric,
    secondary_objective=objectives[0].metric,
    absolute_metrics=["branin", "currin"],
)
render(plot_pareto_frontier(frontier, CI_level=0.90))

from botorch.acquisition.objective import ScalarizedPosteriorTransform
from botorch.acquisition.analytic import UpperConfidenceBound

pt = ScalarizedPosteriorTransform(weights=torch.tensor([0.1, 0.5]))
SUCB = UpperConfidenceBound(gp, beta=0.1, posterior_transform=pt)