"""
This module was copied from the GAUCHE library(https://github.com/leojklarner/gauche/blob/main/gauche/kernels/fingerprint_kernels/base_fingerprint_kernel.py).

GAUCHE was published under the following license (https://github.com/leojklarner/gauche/blob/main/LICENSE):

MIT License

Copyright (c) 2021 Anonymous Authors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

import torch
from gpytorch.kernels import Kernel


def default_postprocess_script(x):
    return x


def batch_tanimoto_sim(
    x1: torch.Tensor, x2: torch.Tensor, eps: float = 1e-6
) -> torch.Tensor:
    """
    Tanimoto between two batched tensors, across last 2 dimensions.
    eps argument ensures numerical stability if all zero tensors are added.
    """
    # Tanimoto distance is proportional to (<x, y>) / (||x||^2 + ||y||^2 - <x, y>) where x and y are bit vectors
    assert x1.ndim >= 2 and x2.ndim >= 2
    dot_prod = torch.matmul(x1, torch.transpose(x2, -1, -2))
    # x1_sum = torch.sum(x1**2, dim=-1, keepdims=True)
    # x2_sum = torch.sum(x2**2, dim=-1, keepdims=True)
    x1_sum = torch.sum(x1**2, dim=-1).unsqueeze(-1)
    x2_sum = torch.sum(x2**2, dim=-1).unsqueeze(-1)
    return (dot_prod + eps) / (
        eps + x1_sum + torch.transpose(x2_sum, -1, -2) - dot_prod
    )


class BitDistance(torch.nn.Module):
    """
    Distance module for bit vector test_kernels.
    """

    def __init__(self, postprocess_script=default_postprocess_script):
        super().__init__()
        self._postprocess = postprocess_script

    def _sim(self, x1, x2, postprocess, x1_eq_x2=False, metric="tanimoto"):
        """
        Computes the similarity between x1 and x2

        Args:
            x1 (Tensor): First set of data where b is a batch dimension. Has shape `n x d` or `b x n x d`
            x2 (Tensor): Second set of data where b is a batch dimension. Has shape `m x d` or `b x m x d`
            postprocess (bool): Whether to apply a postprocess script
            x1_eq_x2 (bool, optional): Is x1 equal to x2. Defaults to False
            metric (str): String specifying the similarity metric. One of ['tanimoto']. Defaults to 'tanimoto'

        Raises:
            RuntimeError: If tanimoto is not used as the similarity metric.

        Returns:
            Tensor: corresponding to the similarity matrix between `x1` and `x2`
        """
        # Branch for Tanimoto metric
        if metric == "tanimoto":
            res = batch_tanimoto_sim(x1, x2)
            res.clamp_min_(0)  # zero out negative values
            return self._postprocess(res) if postprocess else res
        else:
            raise RuntimeError(
                "Similarity metric not supported. Available options are 'tanimoto'"
            )


class BitKernel(Kernel):
    """
     Base class for test_kernels that operate on bit or count vectors such as ECFP fingerprints or RDKit fragments.
     In the typical use case, test_kernels inheriting from this class will specify a similarity metric such as Tanimoto,
     MinMax etc. This kernel does not have an `outputscale` parameter. To add a scaling parameter, decorate this kernel
     with a `gpytorch.test_kernels.ScaleKernel`. This base :class:`BitKernel` class does not include a lengthscale
     parameter `Theta`, in contrast to many common kernel functions.

    Args:
        metric (str): The similarity metric to use. One of ['tanimoto']. Defaults to ''
    """

    def __init__(self, metric="", **kwargs):
        super().__init__(**kwargs)
        self.metric = metric

    def forward(self, x1, x2, **params):
        return self.covar_dist(x1, x2, **params)

    def covar_dist(
        self,
        x1,
        x2,
        last_dim_is_batch=False,
        dist_postprocess_func=default_postprocess_script,
        postprocess=True,
        **params,
    ):
        """
        This is a helper method for computing the bit vector similarity between
        all pairs of points in x1 and x2.

        Args:
            x1 (Tensor): First set of data. Has shape `n x d` or `b1 x ... x bk x n x d`
            x2 (Tensor): Second set of data. Has shape `m x d` or `b1 x ... x bk x m x d`
            last_dim_is_batch (bool, optional): Is the last dimension of the data a batch dimension or not?.
            Defaults to False
            postprocess (bool): Whether to apply a postprocess script

        Returns:
            Tensor: corresponding to the distance matrix between `x1` and `x2`.
            The shape depends on the kernel's mode
            * `diag=False`
            * `diag=False` and `last_dim_is_batch=True`: (`b x d x n x n`)
            * `diag=True`
            * `diag=True` and `last_dim_is_batch=True`: (`b x d x n`)
        """
        if last_dim_is_batch:
            x1 = x1.transpose(-1, -2).unsqueeze(-1)
            x2 = x2.transpose(-1, -2).unsqueeze(-1)

        x1_eq_x2 = torch.equal(x1, x2)

        # torch scripts expect tensors
        postprocess = torch.tensor(postprocess)

        res = None

        # Cache the Distance object or else JIT will recompile every time
        if (
            not self.distance_module
            or self.distance_module._postprocess != dist_postprocess_func
        ):
            self.distance_module = BitDistance(dist_postprocess_func)

        res = self.distance_module._sim(x1, x2, postprocess, x1_eq_x2, self.metric)

        return res
