import os
from contextlib import contextmanager

from ax.utils.testing.mock import fast_botorch_optimize_context_manager
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"

SMOKE_TEST = os.environ.get("SMOKE_TEST")
NUM_EVALS = 10 if SMOKE_TEST else 30


@contextmanager
def dummy_context_manager():
    yield


if SMOKE_TEST:
    fast_smoke_test = fast_botorch_optimize_context_manager
else:
    fast_smoke_test = dummy_context_manager

from botorch.models.gpytorch import GPyTorchModel
from botorch.utils.datasets import SupervisedDataset
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import RBFKernel, ScaleKernel
from gpytorch.likelihoods import GaussianLikelihood
from gpytorch.means import ConstantMean
from gpytorch.models import ExactGP


class SimpleCustomGP(ExactGP, GPyTorchModel):

    _num_outputs = 1  # to inform GPyTorchModel API

    def __init__(self, train_X, train_Y):
        # squeeze output dim before passing train_Y to ExactGP
        super().__init__(train_X, train_Y.squeeze(-1), GaussianLikelihood())
        self.mean_module = ConstantMean()
        self.covar_module = ScaleKernel(
            base_kernel=RBFKernel(ard_num_dims=train_X.shape[-1]),
        )
        self.to(train_X)  # make sure we're on the right device/dtype

    def forward(self, x):
        mean_x = self.mean_module(x)
        covar_x = self.covar_module(x)
        return MultivariateNormal(mean_x, covar_x)

from ax.models.torch.botorch_modular.model import BoTorchModel
from ax.models.torch.botorch_modular.surrogate import Surrogate


ax_model = BoTorchModel(
    surrogate=Surrogate(
        # The model class to use
        botorch_model_class=SimpleCustomGP,
        # Optional, MLL class with which to optimize model parameters
        # mll_class=ExactMarginalLogLikelihood,
        # Optional, dictionary of keyword arguments to model constructor
        # model_options={}
    ),
    # Optional, acquisition function class to use - see custom acquisition tutorial
    # botorch_acqf_class=qExpectedImprovement,
)

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
        ),
        # 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.
            model_kwargs={
                "surrogate": Surrogate(SimpleCustomGP),
            },
        ),
    ]
)

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


# 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="branin_test_experiment",
    parameters=[
        {
            "name": "x1",
            "type": "range",
            # It is crucial to use floats for the bounds, i.e., 0.0 rather than 0.
            # Otherwise, the parameter would be inferred as an integer range.
            "bounds": [-5.0, 10.0],
        },
        {
            "name": "x2",
            "type": "range",
            "bounds": [0.0, 15.0],
        },
    ],
    objectives={
        "branin": ObjectiveProperties(minimize=True),
    },
)
# Setup a function to evaluate the trials
branin = Branin()


def evaluate(parameters):
    x = torch.tensor([[parameters.get(f"x{i+1}") for i in range(2)]])
    # The GaussianLikelihood used by our model infers an observation noise level,
    # so we pass an sem value of NaN to indicate that observation noise is unknown
    return {"branin": (branin(x).item(), float("nan"))}

with fast_smoke_test():
    for i in range(NUM_EVALS):
        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.get_trials_data_frame()

parameters, values = ax_client.get_best_parameters()
print(f"Best parameters: {parameters}")
print(f"Corresponding mean: {values[0]}, covariance: {values[1]}")

from ax.utils.notebook.plotting import render

render(ax_client.get_contour_plot())

best_parameters, values = ax_client.get_best_parameters()
best_parameters, values[0]

render(ax_client.get_optimization_trace(objective_optimum=0.397887))

import pandas as pd
import torch
from ax import (
    Data,
    Experiment,
    Metric,
    Objective,
    OptimizationConfig,
    ParameterType,
    RangeParameter,
    Runner,
    SearchSpace,
)
from botorch.test_functions import Branin


branin_func = Branin()

# For our purposes, the metric is a wrapper that structures the function output.
class BraninMetric(Metric):
    def fetch_trial_data(self, trial):
        records = []
        for arm_name, arm in trial.arms_by_name.items():
            params = arm.parameters
            tensor_params = torch.tensor([params["x1"], params["x2"]])
            records.append(
                {
                    "arm_name": arm_name,
                    "metric_name": self.name,
                    "trial_index": trial.index,
                    "mean": branin_func(tensor_params),
                    "sem": float("nan"),  # SEM (observation noise) - NaN indicates unknown
                }
            )
        return Data(df=pd.DataFrame.from_records(records))


# Search space defines the parameters, their types, and acceptable values.
search_space = SearchSpace(
    parameters=[
        RangeParameter(name="x1", parameter_type=ParameterType.FLOAT, lower=-5, upper=10),
        RangeParameter(name="x2", parameter_type=ParameterType.FLOAT, lower=0, upper=15),
    ]
)

optimization_config = OptimizationConfig(
    objective=Objective(
        metric=BraninMetric(name="branin_metric", lower_is_better=True),
        minimize=True,  # This is optional since we specified `lower_is_better=True`
    )
)


class MyRunner(Runner):
    def run(self, trial):
        trial_metadata = {"name": str(trial.index)}
        return trial_metadata


exp = Experiment(
    name="branin_experiment",
    search_space=search_space,
    optimization_config=optimization_config,
    runner=MyRunner(),
)

from ax.modelbridge.registry import Models


sobol = Models.SOBOL(exp.search_space)

for i in range(5):
    trial = exp.new_trial(generator_run=sobol.gen(1))
    trial.run()
    trial.mark_completed()

with fast_smoke_test():
    for i in range(NUM_EVALS - 5):
        model_bridge = Models.BOTORCH_MODULAR(
            experiment=exp,
            data=exp.fetch_data(),
            surrogate=Surrogate(SimpleCustomGP),
        )
        trial = exp.new_trial(generator_run=model_bridge.gen(1))
        trial.run()
        trial.mark_completed()

exp.trials

exp.fetch_data().df

import numpy as np
from ax.plot.trace import optimization_trace_single_method


# `plot_single_method` expects a 2-d array of means, because it expects to average means from multiple
# optimization runs, so we wrap out best objectives array in another array.
objective_means = np.array([[trial.objective_mean for trial in exp.trials.values()]])
best_objective_plot = optimization_trace_single_method(
    y=np.minimum.accumulate(objective_means, axis=1),
    optimum=0.397887,  # Known minimum objective for Branin function.
)
render(best_objective_plot)