#!/usr/bin/env python3

import torch

from ..distributions import MultivariateNormal
from ..lazy import InterpolatedLazyTensor
from ..utils.broadcasting import _mul_broadcast_shape
from ..utils.interpolation import Interpolation, left_interp
from ..utils.memoize import cached
from ._variational_strategy import _VariationalStrategy


class GridInterpolationVariationalStrategy(_VariationalStrategy):
    """
    This strategy constrains the inducing points to a grid and applies a deterministic
    relationship between :math:`\mathbf f` and :math:`\mathbf u`.
    It was introduced by `Wilson et al. (2016)`_.

    Here, the inducing points are not learned. Instead, the strategy
    automatically creates inducing points based on a set of grid sizes and grid
    bounds.

    .. _Wilson et al. (2016):
        https://arxiv.org/abs/1611.00336

    :param ~gpytorch.models.ApproximateGP model: Model this strategy is applied to.
        Typically passed in when the VariationalStrategy is created in the
        __init__ method of the user defined model.
    :param int grid_size: Size of the grid
    :param list grid_bounds: Bounds of each dimension of the grid (should be a list of (float, float) tuples)
    :param ~gpytorch.variational.VariationalDistribution variational_distribution: A
        VariationalDistribution object that represents the form of the variational distribution :math:`q(\mathbf u)`
    """

    def __init__(self, model, grid_size, grid_bounds, variational_distribution):
        grid = torch.zeros(grid_size, len(grid_bounds))
        for i in range(len(grid_bounds)):
            grid_diff = float(grid_bounds[i][1] - grid_bounds[i][0]) / (grid_size - 2)
            grid[:, i] = torch.linspace(grid_bounds[i][0] - grid_diff, grid_bounds[i][1] + grid_diff, grid_size)

        inducing_points = torch.zeros(int(pow(grid_size, len(grid_bounds))), len(grid_bounds))
        prev_points = None
        for i in range(len(grid_bounds)):
            for j in range(grid_size):
                inducing_points[j * grid_size ** i : (j + 1) * grid_size ** i, i].fill_(grid[j, i])
                if prev_points is not None:
                    inducing_points[j * grid_size ** i : (j + 1) * grid_size ** i, :i].copy_(prev_points)
            prev_points = inducing_points[: grid_size ** (i + 1), : (i + 1)]

        super(GridInterpolationVariationalStrategy, self).__init__(
            model, inducing_points, variational_distribution, learn_inducing_locations=False
        )
        object.__setattr__(self, "model", model)

        self.register_buffer("grid", grid)

    def _compute_grid(self, inputs):
        n_data, n_dimensions = inputs.size(-2), inputs.size(-1)
        batch_shape = inputs.shape[:-2]

        inputs = inputs.reshape(-1, n_dimensions)
        interp_indices, interp_values = Interpolation().interpolate(self.grid, inputs)
        interp_indices = interp_indices.view(*batch_shape, n_data, -1)
        interp_values = interp_values.view(*batch_shape, n_data, -1)

        if (interp_indices.dim() - 2) != len(self._variational_distribution.batch_shape):
            batch_shape = _mul_broadcast_shape(interp_indices.shape[:-2], self._variational_distribution.batch_shape)
            interp_indices = interp_indices.expand(*batch_shape, *interp_indices.shape[-2:])
            interp_values = interp_values.expand(*batch_shape, *interp_values.shape[-2:])
        return interp_indices, interp_values

    @property
    @cached(name="prior_distribution_memo")
    def prior_distribution(self):
        out = self.model.forward(self.inducing_points)
        res = MultivariateNormal(out.mean, out.lazy_covariance_matrix.add_jitter())
        return res

    def forward(self, x, inducing_points, inducing_values, variational_inducing_covar=None):
        if variational_inducing_covar is None:
            raise RuntimeError(
                "GridInterpolationVariationalStrategy is only compatible with Gaussian variational "
                f"distributions. Got ({self.variational_distribution.__class__.__name__}."
            )

        variational_distribution = self.variational_distribution

        # Get interpolations
        interp_indices, interp_values = self._compute_grid(x)

        # Compute test mean
        # Left multiply samples by interpolation matrix
        predictive_mean = left_interp(interp_indices, interp_values, inducing_values.unsqueeze(-1))
        predictive_mean = predictive_mean.squeeze(-1)

        # Compute test covar
        predictive_covar = InterpolatedLazyTensor(
            variational_distribution.lazy_covariance_matrix,
            interp_indices,
            interp_values,
            interp_indices,
            interp_values,
        )
        output = MultivariateNormal(predictive_mean, predictive_covar)
        return output

#!/usr/bin/env python3

import torch

from ..distributions import MultivariateNormal
from ..lazy import InterpolatedLazyTensor
from ..utils.broadcasting import _mul_broadcast_shape
from ..utils.interpolation import Interpolation, left_interp
from ..utils.memoize import cached
from ._variational_strategy import _VariationalStrategy


class GridInterpolationVariationalStrategy(_VariationalStrategy):
    """
    This strategy constrains the inducing points to a grid and applies a deterministic
    relationship between :math:`\mathbf f` and :math:`\mathbf u`.
    It was introduced by `Wilson et al. (2016)`_.

    Here, the inducing points are not learned. Instead, the strategy
    automatically creates inducing points based on a set of grid sizes and grid
    bounds.

    .. _Wilson et al. (2016):
        https://arxiv.org/abs/1611.00336

    :param ~gpytorch.models.ApproximateGP model: Model this strategy is applied to.
        Typically passed in when the VariationalStrategy is created in the
        __init__ method of the user defined model.
    :param int grid_size: Size of the grid
    :param list grid_bounds: Bounds of each dimension of the grid (should be a list of (float, float) tuples)
    :param ~gpytorch.variational.VariationalDistribution variational_distribution: A
        VariationalDistribution object that represents the form of the variational distribution :math:`q(\mathbf u)`
    """

    def __init__(self, model, grid_size, grid_bounds, variational_distribution):
        grid = torch.zeros(grid_size, len(grid_bounds))
        for i in range(len(grid_bounds)):
            grid_diff = float(grid_bounds[i][1] - grid_bounds[i][0]) / (grid_size - 2)
            grid[:, i] = torch.linspace(grid_bounds[i][0] - grid_diff, grid_bounds[i][1] + grid_diff, grid_size)

        inducing_points = torch.zeros(int(pow(grid_size, len(grid_bounds))), len(grid_bounds))
        prev_points = None
        for i in range(len(grid_bounds)):
            for j in range(grid_size):
                inducing_points[j * grid_size ** i : (j + 1) * grid_size ** i, i].fill_(grid[j, i])
                if prev_points is not None:
                    inducing_points[j * grid_size ** i : (j + 1) * grid_size ** i, :i].copy_(prev_points)
            prev_points = inducing_points[: grid_size ** (i + 1), : (i + 1)]

        super(GridInterpolationVariationalStrategy, self).__init__(
            model, inducing_points, variational_distribution, learn_inducing_locations=False
        )
        object.__setattr__(self, "model", model)

        self.register_buffer("grid", grid)

    def _compute_grid(self, inputs):
        n_data, n_dimensions = inputs.size(-2), inputs.size(-1)
        batch_shape = inputs.shape[:-2]

        inputs = inputs.reshape(-1, n_dimensions)
        interp_indices, interp_values = Interpolation().interpolate(self.grid, inputs)
        interp_indices = interp_indices.view(*batch_shape, n_data, -1)
        interp_values = interp_values.view(*batch_shape, n_data, -1)

        if (interp_indices.dim() - 2) != len(self._variational_distribution.batch_shape):
            batch_shape = _mul_broadcast_shape(interp_indices.shape[:-2], self._variational_distribution.batch_shape)
            interp_indices = interp_indices.expand(*batch_shape, *interp_indices.shape[-2:])
            interp_values = interp_values.expand(*batch_shape, *interp_values.shape[-2:])
        return interp_indices, interp_values

    @property
    @cached(name="prior_distribution_memo")
    def prior_distribution(self):
        out = self.model.forward(self.inducing_points)
        res = MultivariateNormal(out.mean, out.lazy_covariance_matrix.add_jitter())
        return res

    def forward(self, x, inducing_points, inducing_values, variational_inducing_covar=None):
        if variational_inducing_covar is None:
            raise RuntimeError(
                "GridInterpolationVariationalStrategy is only compatible with Gaussian variational "
                f"distributions. Got ({self.variational_distribution.__class__.__name__}."
            )

        variational_distribution = self.variational_distribution

        # Get interpolations
        interp_indices, interp_values = self._compute_grid(x)

        # Compute test mean
        # Left multiply samples by interpolation matrix
        predictive_mean = left_interp(interp_indices, interp_values, inducing_values.unsqueeze(-1))
        predictive_mean = predictive_mean.squeeze(-1)

        # Compute test covar
        predictive_covar = InterpolatedLazyTensor(
            variational_distribution.lazy_covariance_matrix,
            interp_indices,
            interp_values,
            interp_indices,
            interp_values,
        )
        output = MultivariateNormal(predictive_mean, predictive_covar)
        return output

#!/usr/bin/env python3 This strategy constrains the inducing points to a grid and applies a deterministic     relationship between :math:`\mathbf f` and :math:`\mathbf u`.     It was introduced by `Wilson et al. (2016)`_.      Here, the inducing points are not learned. Instead, the strategy     automatically creates inducing points based on a set of grid sizes and grid     bounds.      .. _Wilson et al. (2016):         https://arxiv.org/abs/1611.00336      :param ~gpytorch.models.ApproximateGP model: Model this strategy is applied to.         Typically passed in when the VariationalStrategy is created in the         __init__ method of the user defined model.     :param int grid_size: Size of the grid     :param list grid_bounds: Bounds of each dimension of the grid (should be a list of (float, float) tuples)     :param ~gpytorch.variational.VariationalDistribution variational_distribution: A         VariationalDistribution object that represents the form of the variational distribution :math:`q(\mathbf u)` # Get interpolations # Compute test mean # Left multiply samples by interpolation matrix # Compute test covar