#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

r"""Abstract base module for all BoTorch models.

This module contains `Model`, the abstract base class for all BoTorch models,
and `ModelList`, a container for a list of Models.
"""

from __future__ import annotations

import warnings
from abc import ABC, abstractmethod
from collections import defaultdict
from copy import deepcopy
from typing import (
    Any,
    Callable,
    Dict,
    Hashable,
    List,
    Mapping,
    Optional,
    TYPE_CHECKING,
    TypeVar,
    Union,
)

import numpy as np
import torch
from botorch import settings
from botorch.exceptions.errors import BotorchTensorDimensionError, InputDataError
from botorch.logging import shape_to_str
from botorch.models.utils.assorted import fantasize as fantasize_flag
from botorch.posteriors import Posterior, PosteriorList
from botorch.sampling.base import MCSampler
from botorch.sampling.list_sampler import ListSampler
from botorch.utils.datasets import SupervisedDataset
from botorch.utils.transforms import is_fully_bayesian
from torch import Tensor
from torch.nn import Module, ModuleDict, ModuleList

if TYPE_CHECKING:
    from botorch.acquisition.objective import PosteriorTransform  # pragma: no cover

TFantasizeMixin = TypeVar("TFantasizeMixin", bound="FantasizeMixin")


class Model(Module, ABC):
    r"""Abstract base class for BoTorch models.

    The `Model` base class cannot be used directly; it only defines an API for other
    BoTorch models.

    `Model` subclasses `torch.nn.Module`. While a `Module` is most typically
    encountered as a representation of a neural network layer, it can be used more
    generally: see
    `documentation <https://pytorch.org/tutorials/beginner/examples_nn/polynomial_module.html>`_
    on custom NN Modules.

    `Module` provides several pieces of useful functionality: A `Model`'s attributes of
    `Tensor` or `Module` type are automatically registered so they can be moved and/or
    cast with the `to` method, automatically differentiated, and used with CUDA.

    Args:
        _has_transformed_inputs: A boolean denoting whether `train_inputs` are currently
            stored as transformed or not.
        _original_train_inputs: A Tensor storing the original train inputs for use in
            `_revert_to_original_inputs`. Note that this is necessary since
            transform / untransform cycle introduces numerical errors which lead
            to upstream errors during training.
    """  # noqa: E501

    _has_transformed_inputs: bool = False
    _original_train_inputs: Optional[Tensor] = None

    @abstractmethod
    def posterior(
        self,
        X: Tensor,
        output_indices: Optional[List[int]] = None,
        observation_noise: bool = False,
        posterior_transform: Optional[PosteriorTransform] = None,
        **kwargs: Any,
    ) -> Posterior:
        r"""Computes the posterior over model outputs at the provided points.

        Note: The input transforms should be applied here using
            `self.transform_inputs(X)` after the `self.eval()` call and before
            any `model.forward` or `model.likelihood` calls.

        Args:
            X: A `b x q x d`-dim Tensor, where `d` is the dimension of the
                feature space, `q` is the number of points considered jointly,
                and `b` is the batch dimension.
            output_indices: A list of indices, corresponding to the outputs over
                which to compute the posterior (if the model is multi-output).
                Can be used to speed up computation if only a subset of the
                model's outputs are required for optimization. If omitted,
                computes the posterior over all model outputs.
            observation_noise: If True, add observation noise to the posterior.
            posterior_transform: An optional PosteriorTransform.

        Returns:
            A `Posterior` object, representing a batch of `b` joint distributions
            over `q` points and `m` outputs each.
        """
        pass  # pragma: no cover

    @property
    def batch_shape(self) -> torch.Size:
        r"""The batch shape of the model.

        This is a batch shape from an I/O perspective, independent of the internal
        representation of the model (as e.g. in BatchedMultiOutputGPyTorchModel).
        For a model with `m` outputs, a `test_batch_shape x q x d`-shaped input `X`
        to the `posterior` method returns a Posterior object over an output of
        shape `broadcast(test_batch_shape, model.batch_shape) x q x m`.
        """
        cls_name = self.__class__.__name__
        raise NotImplementedError(f"{cls_name} does not define batch_shape property")

    @property
    def num_outputs(self) -> int:
        r"""The number of outputs of the model."""
        cls_name = self.__class__.__name__
        raise NotImplementedError(f"{cls_name} does not define num_outputs property")

    def subset_output(self, idcs: List[int]) -> Model:
        r"""Subset the model along the output dimension.

        Args:
            idcs: The output indices to subset the model to.

        Returns:
            A `Model` object of the same type and with the same parameters as
            the current model, subset to the specified output indices.
        """
        raise NotImplementedError

    def condition_on_observations(self, X: Tensor, Y: Tensor, **kwargs: Any) -> Model:
        r"""Condition the model on new observations.

        Args:
            X: A `batch_shape x n' x d`-dim Tensor, where `d` is the dimension of
                the feature space, `n'` is the number of points per batch, and
                `batch_shape` is the batch shape (must be compatible with the
                batch shape of the model).
            Y: A `batch_shape' x n' x m`-dim Tensor, where `m` is the number of
                model outputs, `n'` is the number of points per batch, and
                `batch_shape'` is the batch shape of the observations.
                `batch_shape'` must be broadcastable to `batch_shape` using
                standard broadcasting semantics. If `Y` has fewer batch dimensions
                than `X`, it is assumed that the missing batch dimensions are
                the same for all `Y`.

        Returns:
            A `Model` object of the same type, representing the original model
            conditioned on the new observations `(X, Y)` (and possibly noise
            observations passed in via kwargs).
        """
        raise NotImplementedError(
            f"`condition_on_observations` not implemented for {self.__class__.__name__}"
        )

    @classmethod
    def construct_inputs(
        cls,
        training_data: Union[SupervisedDataset, Dict[Hashable, SupervisedDataset]],
        **kwargs: Any,
    ) -> Dict[str, Any]:
        r"""Construct `Model` keyword arguments from a dict of `SupervisedDataset`."""
        from botorch.models.utils.parse_training_data import parse_training_data

        return parse_training_data(cls, training_data, **kwargs)

    def transform_inputs(
        self,
        X: Tensor,
        input_transform: Optional[Module] = None,
    ) -> Tensor:
        r"""Transform inputs.

        Args:
            X: A tensor of inputs
            input_transform: A Module that performs the input transformation.

        Returns:
            A tensor of transformed inputs
        """
        if input_transform is not None:
            input_transform.to(X)
            return input_transform(X)
        try:
            return self.input_transform(X)
        except AttributeError:
            return X

    def _set_transformed_inputs(self) -> None:
        r"""Update training inputs with transformed inputs."""
        if hasattr(self, "input_transform") and not self._has_transformed_inputs:
            if hasattr(self, "train_inputs"):
                self._original_train_inputs = self.train_inputs[0]
                with torch.no_grad():
                    X_tf = self.input_transform.preprocess_transform(
                        self.train_inputs[0]
                    )
                self.set_train_data(X_tf, strict=False)
                self._has_transformed_inputs = True
            else:
                warnings.warn(
                    "Could not update `train_inputs` with transformed inputs "
                    f"since {self.__class__.__name__} does not have a `train_inputs` "
                    "attribute. Make sure that the `input_transform` is applied to "
                    "both the train inputs and test inputs.",
                    RuntimeWarning,
                )

    def _revert_to_original_inputs(self) -> None:
        r"""Revert training inputs back to original."""
        if hasattr(self, "input_transform") and self._has_transformed_inputs:
            self.set_train_data(self._original_train_inputs, strict=False)
            self._has_transformed_inputs = False

    def eval(self) -> Model:
        r"""Puts the model in `eval` mode and sets the transformed inputs."""
        self._set_transformed_inputs()
        return super().eval()

    def train(self, mode: bool = True) -> Model:
        r"""Put the model in `train` mode. Reverts to the original inputs if in `train`
        mode (`mode=True`) or sets transformed inputs if in `eval` mode (`mode=False`).

        Args:
            mode: A boolean denoting whether to put in `train` or `eval` mode.
                If `False`, model is put in `eval` mode.
        """
        if mode:
            self._revert_to_original_inputs()
        else:
            self._set_transformed_inputs()
        return super().train(mode=mode)


class FantasizeMixin(ABC):
    """
    Mixin to add a `fantasize` method to a `Model`.

    Example:
        class BaseModel:
            def __init__(self, ...):
            def condition_on_observations(self, ...):
            def posterior(self, ...):
            def transform_inputs(self, ...):

        class ModelThatCanFantasize(BaseModel, FantasizeMixin):
            def __init__(self, args):
                super().__init__(args)

        model = ModelThatCanFantasize(...)
        model.fantasize(X)
    """

    @abstractmethod
    def condition_on_observations(
        self: TFantasizeMixin, X: Tensor, Y: Tensor, **kwargs: Any
    ) -> TFantasizeMixin:
        """
        Classes that inherit from `FantasizeMixin` must implement
        a `condition_on_observations` method.
        """

    @abstractmethod
    def posterior(
        self,
        X: Tensor,
        *args,
        observation_noise: bool = False,
        **kwargs: Any,
    ) -> Posterior:
        """
        Classes that inherit from `FantasizeMixin` must implement
        a `posterior` method.
        """

    @abstractmethod
    def transform_inputs(
        self,
        X: Tensor,
        input_transform: Optional[Module] = None,
    ) -> Tensor:
        """
        Classes that inherit from `FantasizeMixin` must implement
        a `transform_inputs` method.
        """

    # When Python 3.11 arrives we can start annotating return types like
    # this as
    # 'Self', but at this point the verbose 'T...' syntax is needed.
    def fantasize(
        self: TFantasizeMixin,
        # TODO: see if any of these can be imported only if TYPE_CHECKING
        X: Tensor,
        sampler: MCSampler,
        observation_noise: bool = True,
        **kwargs: Any,
    ) -> TFantasizeMixin:
        r"""Construct a fantasy model.

        Constructs a fantasy model in the following fashion:
        (1) compute the model posterior at `X` (including observation noise if
        `observation_noise=True`).
        (2) sample from this posterior (using `sampler`) to generate "fake"
        observations.
        (3) condition the model on the new fake observations.

        Args:
            X: A `batch_shape x n' x d`-dim Tensor, where `d` is the dimension of
                the feature space, `n'` is the number of points per batch, and
                `batch_shape` is the batch shape (must be compatible with the
                batch shape of the model).
            sampler: The sampler used for sampling from the posterior at `X`.
            observation_noise: If True, include observation noise.
            kwargs: Will be passed to `model.condition_on_observations`

        Returns:
            The constructed fantasy model.
        """
        # if the inputs are empty, expand the inputs
        if X.shape[-2] == 0:
            output_shape = (
                sampler.sample_shape
                + X.shape[:-2]
                + self.batch_shape
                + torch.Size([0, self.num_outputs])
            )
            return self.condition_on_observations(
                X=self.transform_inputs(X),
                Y=torch.empty(output_shape, dtype=X.dtype, device=X.device),
                **kwargs,
            )
        propagate_grads = kwargs.pop("propagate_grads", False)
        with fantasize_flag():
            with settings.propagate_grads(propagate_grads):
                post_X = self.posterior(X, observation_noise=observation_noise)
            Y_fantasized = sampler(post_X)  # num_fantasies x batch_shape x n' x m
            return self.condition_on_observations(
                X=self.transform_inputs(X), Y=Y_fantasized, **kwargs
            )


class ModelList(Model):
    r"""A multi-output Model represented by a list of independent models.

    All BoTorch models are acceptable as inputs. The cost of this flexibility is
    that `ModelList` does not support all methods that may be implemented by its
    component models. One use case for `ModelList` is combining a regression
    model and a deterministic model in one multi-output container model, e.g.
    for cost-aware or multi-objective optimization where one of the outcomes is
    a deterministic function of the inputs.
    """

    def __init__(self, *models: Model) -> None:
        r"""
        Args:
            *models: A variable number of models.

        Example:
            >>> m_1 = SingleTaskGP(train_X, train_Y)
            >>> m_2 = GenericDeterministicModel(lambda x: x.sum(dim=-1))
            >>> m_12 = ModelList(m_1, m_2)
            >>> m_12.posterior(test_X)
        """
        super().__init__()
        self.models = ModuleList(models)

    def _get_group_subset_indices(
        self, idcs: Optional[List[int]]
    ) -> Dict[int, List[int]]:
        r"""Convert global subset indices to indices for the individual models.

        Args:
            idcs: A list of indices to which the `ModelList` model is to be
                subset to.

        Returns:
            A dictionary mapping model indices to subset indices of the
                respective model in the `ModelList`.
        """
        if idcs is None:
            return {i: None for i in range(len(self.models))}
        output_sizes = [model.num_outputs for model in self.models]
        cum_output_sizes = np.cumsum(output_sizes)
        idcs = [idx % cum_output_sizes[-1] for idx in idcs]
        group_indices: Dict[int, List[int]] = defaultdict(list)
        for idx in idcs:
            grp_idx = int(np.argwhere(idx < cum_output_sizes)[0])
            sub_idx = idx - int(np.sum(output_sizes[:grp_idx]))
            group_indices[grp_idx].append(sub_idx)
        return group_indices

    def posterior(
        self,
        X: Tensor,
        output_indices: Optional[List[int]] = None,
        observation_noise: Union[bool, Tensor] = False,
        posterior_transform: Optional[Callable[[PosteriorList], Posterior]] = None,
        **kwargs: Any,
    ) -> Posterior:
        r"""Computes the posterior over model outputs at the provided points.

        Note: The input transforms should be applied here using
            `self.transform_inputs(X)` after the `self.eval()` call and before
            any `model.forward` or `model.likelihood` calls.

        Args:
            X: A `b x q x d`-dim Tensor, where `d` is the dimension of the
                feature space, `q` is the number of points considered jointly,
                and `b` is the batch dimension.
            output_indices: A list of indices, corresponding to the outputs over
                which to compute the posterior (if the model is multi-output).
                Can be used to speed up computation if only a subset of the
                model's outputs are required for optimization. If omitted,
                computes the posterior over all model outputs.
            observation_noise: If True, add the observation noise from the
                respective likelihoods to the posterior. If a Tensor of shape
                `(batch_shape) x q x m`, use it directly as the observation
                noise (with `observation_noise[...,i]` added to the posterior
                of the `i`-th model).
            posterior_transform: An optional PosteriorTransform.

        Returns:
            A `Posterior` object, representing a batch of `b` joint distributions
            over `q` points and `m` outputs each.
        """
        group_indices = self._get_group_subset_indices(idcs=output_indices)
        posteriors = []
        for i, idcs in group_indices.items():
            if isinstance(observation_noise, Tensor):
                if idcs is None:
                    start_idx = sum(m.num_outputs for m in self.models[:i])
                    end_idx = start_idx + self.models[i].num_outputs
                    idcs = list(range(start_idx, end_idx))
                obs_noise = observation_noise[..., idcs]
            else:
                obs_noise = observation_noise
            posteriors.append(
                self.models[i].posterior(
                    X=X, output_indices=idcs, observation_noise=obs_noise
                )
            )
        posterior = PosteriorList(*posteriors)
        if posterior_transform is not None:
            posterior = posterior_transform(posterior)
        return posterior

    @property
    def batch_shape(self) -> torch.Size:
        r"""The batch shape of the model.

        This is a batch shape from an I/O perspective, independent of the internal
        representation of the model (as e.g. in BatchedMultiOutputGPyTorchModel).
        For a model with `m` outputs, a `test_batch_shape x q x d`-shaped input `X`
        to the `posterior` method returns a Posterior object over an output of
        shape `broadcast(test_batch_shape, model.batch_shape) x q x m`.
        """
        batch_shape = self.models[0].batch_shape
        if all(batch_shape == m.batch_shape for m in self.models[1:]):
            return batch_shape
        # TODO: Allow broadcasting of model batch shapes
        raise NotImplementedError(
            f"`{self.__class__.__name__}.batch_shape` is only supported if all "
            "constituent models have the same `batch_shape`."
        )

    @property
    def num_outputs(self) -> int:
        r"""The number of outputs of the model.

        Equal to the sum of the number of outputs of the individual models
        in the ModelList.
        """
        return sum(model.num_outputs for model in self.models)

    def subset_output(self, idcs: List[int]) -> Model:
        r"""Subset the model along the output dimension.

        Args:
            idcs: The output indices to subset the model to. Relative to the
                overall number of outputs of the model.

        Returns:
            A `Model` (either a `ModelList` or one of the submodels) with
            the outputs subset to the indices in `idcs`.

        Internally, this drops (if single-output) or subsets (if multi-output)
        the constitutent models and returns them as a `ModelList`. If the
        result is a single (possibly subset) model from the list, returns this
        model (instead of forming a degenerate singe-model `ModelList`).
        For instance, if `m = ModelList(m1, m2)` with `m1` a two-output model
        and `m2` a single-output model, then `m.subset_output([1]) ` will return
        the model `m1` subset to its second output.
        """
        group_indices = self._get_group_subset_indices(idcs=idcs)
        subset_models = [
            deepcopy(self.models[grp_idx].subset_output(idcs=sub_idcs))
            for grp_idx, sub_idcs in group_indices.items()
        ]
        if len(subset_models) == 1:
            return subset_models[0]
        return self.__class__(*subset_models)

    def transform_inputs(self, X: Tensor) -> List[Tensor]:
        r"""Individually transform the inputs for each model.

        Args:
            X: A tensor of inputs.

        Returns:
            A list of tensors of transformed inputs.
        """
        transformed_X_list = []
        for model in self.models:
            try:
                transformed_X_list.append(model.input_transform(X))
            except AttributeError:
                transformed_X_list.append(X)
        return transformed_X_list

    def load_state_dict(
        self, state_dict: Mapping[str, Any], strict: bool = True
    ) -> None:
        """Initialize the fully Bayesian models before loading the state dict."""
        for i, m in enumerate(self.models):
            if is_fully_bayesian(m):
                filtered_dict = {
                    k.replace(f"models.{i}.", ""): v
                    for k, v in state_dict.items()
                    if k.startswith(f"models.{i}.")
                }
                m.load_state_dict(filtered_dict)
        super().load_state_dict(state_dict=state_dict, strict=strict)

    def fantasize(
        self,
        X: Tensor,
        sampler: MCSampler,
        observation_noise: bool = True,
        evaluation_mask: Optional[Tensor] = None,
        **kwargs: Any,
    ) -> Model:
        r"""Construct a fantasy model.

        Constructs a fantasy model in the following fashion:
        (1) compute the model posterior at `X` (including observation noise if
        `observation_noise=True`).
        (2) sample from this posterior (using `sampler`) to generate "fake"
        observations.
        (3) condition the model on the new fake observations.

        Args:
            X: A `batch_shape x n' x d`-dim Tensor, where `d` is the dimension of
                the feature space, `n'` is the number of points per batch, and
                `batch_shape` is the batch shape (must be compatible with the
                batch shape of the model).
            sampler: The sampler used for sampling from the posterior at `X`. If
                evaluation_mask is not None, this must be a `ListSampler`.
            observation_noise: If True, include observation noise.
            evaluation_mask: A `n' x m`-dim tensor of booleans indicating which
                outputs should be fantasized for a given design. This uses the same
                evaluation mask for all batches.

        Returns:
            The constructed fantasy model.
        """
        if evaluation_mask is not None:
            if evaluation_mask.ndim != 2 or evaluation_mask.shape != torch.Size(
                [X.shape[-2], self.num_outputs]
            ):
                raise BotorchTensorDimensionError(
                    f"Expected evaluation_mask of shape `{X.shape[0]} "
                    f"x {self.num_outputs}`, but got "
                    f"{shape_to_str(evaluation_mask.shape)}."
                )
            if not isinstance(sampler, ListSampler):
                raise ValueError("Decoupled fantasization requires a list of samplers.")

        fant_models = []
        X_i = X
        for i in range(self.num_outputs):
            # get the inputs to fantasize at for output i
            if evaluation_mask is not None:
                mask_i = evaluation_mask[:, i]
                X_i = X[..., mask_i, :]
                # TODO (T158701749): implement a QMC DecoupledSampler that draws all
                # samples from a single Sobol sequence or consider requiring that the
                # sampling is IID to ensure good coverage.
                sampler_i = sampler.samplers[i]
            else:
                sampler_i = sampler
            fant_model = self.models[i].fantasize(
                X=X_i,
                sampler=sampler_i,
                observation_noise=observation_noise,
                **kwargs,
            )
            fant_models.append(fant_model)
        return self.__class__(*fant_models)


class ModelDict(ModuleDict):
    r"""A lightweight container mapping model names to models."""

    def __init__(self, **models: Model) -> None:
        r"""Initialize a `ModelDict`.

        Args:
            models: An arbitrary number of models. Each model can be any type
                of BoTorch `Model`, including multi-output models and `ModelList`.
        """
        if any(not isinstance(m, Model) for m in models.values()):
            raise InputDataError(
                f"Expected all models to be a BoTorch `Model`. Got {models}."
            )
        super().__init__(modules=models)
