#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.

import math
import warnings
import torch


class Transform3d:
    """
    A Transform3d object encapsulates a batch of N 3D transformations, and knows
    how to transform points and normal vectors. Suppose that t is a Transform3d;
    then we can do the following:

    .. code-block:: python

        N = len(t)
        points = torch.randn(N, P, 3)
        normals = torch.randn(N, P, 3)
        points_transformed = t.transform_points(points)    # => (N, P, 3)
        normals_transformed = t.transform_points(normals)  # => (N, P, 3)


    BROADCASTING
    Transform3d objects supports broadcasting. Suppose that t1 and tN are
    Transform3D objects with len(t1) == 1 and len(tN) == N respectively. Then we
    can broadcast transforms like this:

    .. code-block:: python

        t1.transform_points(torch.randn(P, 3))     # => (P, 3)
        t1.transform_points(torch.randn(1, P, 3))  # => (1, P, 3)
        t1.transform_points(torch.randn(M, P, 3))  # => (M, P, 3)
        tN.transform_points(torch.randn(P, 3))     # => (N, P, 3)
        tN.transform_points(torch.randn(1, P, 3))  # => (N, P, 3)


    COMBINING TRANSFORMS
    Transform3d objects can be combined in two ways: composing and stacking.
    Composing is function composition. Given Transform3d objects t1, t2, t3,
    the following all compute the same thing:

    .. code-block:: python

        y1 = t3.transform_points(t2.transform_points(t2.transform_points(x)))
        y2 = t1.compose(t2).compose(t3).transform_points()
        y3 = t1.compose(t2, t3).transform_points()


    Composing transforms should broadcast.

    .. code-block:: python

        if len(t1) == 1 and len(t2) == N, then len(t1.compose(t2)) == N.

    We can also stack a sequence of Transform3d objects, which represents
    composition along the batch dimension; then the following should compute the
    same thing.

    .. code-block:: python

        N, M = len(tN), len(tM)
        xN = torch.randn(N, P, 3)
        xM = torch.randn(M, P, 3)
        y1 = torch.cat([tN.transform_points(xN), tM.transform_points(xM)], dim=0)
        y2 = tN.stack(tM).transform_points(torch.cat([xN, xM], dim=0))

    BUILDING TRANSFORMS
    We provide convenience methods for easily building Transform3d objects
    as compositions of basic transforms.

    .. code-block:: python

        # Scale by 0.5, then translate by (1, 2, 3)
        t1 = Transform3d().scale(0.5).translate(1, 2, 3)

        # Scale each axis by a different amount, then translate, then scale
        t2 = Transform3d().scale(1, 3, 3).translate(2, 3, 1).scale(2.0)

        t3 = t1.compose(t2)
        tN = t1.stack(t3, t3)


    BACKPROP THROUGH TRANSFORMS
    When building transforms, we can also parameterize them by Torch tensors;
    in this case we can backprop through the construction and application of
    Transform objects, so they could be learned via gradient descent or
    predicted by a neural network.

    .. code-block:: python

        s1_params = torch.randn(N, requires_grad=True)
        t_params = torch.randn(N, 3, requires_grad=True)
        s2_params = torch.randn(N, 3, requires_grad=True)

        t = Transform3d().scale(s1_params).translate(t_params).scale(s2_params)
        x = torch.randn(N, 3)
        y = t.transform_points(x)
        loss = compute_loss(y)
        loss.backward()

        with torch.no_grad():
            s1_params -= lr * s1_params.grad
            t_params -= lr * t_params.grad
            s2_params -= lr * s2_params.grad
    """

    def __init__(self, dtype=torch.float32, device="cpu"):
        """
        This class assumes a row major ordering for all matrices.
        """
        self._matrix = torch.eye(4, dtype=dtype, device=device).view(1, 4, 4)
        self._transforms = []  # store transforms to compose
        self._lu = None
        self.device = device

    def __len__(self):
        return self.get_matrix().shape[0]

    def compose(self, *others):
        """
        Return a new Transform3d with the tranforms to compose stored as
        an internal list.

        Args:
            *others: Any number of Transform3d objects

        Returns:
            A new Transform3d with the stored transforms
        """
        out = Transform3d(device=self.device)
        out._matrix = self._matrix.clone()
        for other in others:
            if not isinstance(other, Transform3d):
                msg = "Only possible to compose Transform3d objects; got %s"
                raise ValueError(msg % type(other))
        out._transforms = self._transforms + list(others)
        return out

    def get_matrix(self):
        """
        Return a matrix which is the result of composing this transform
        with others stored in self.transforms. Where necessary transforms
        are broadcast against each other.
        For example, if self.transforms contains transforms t1, t2, and t3, and
        given a set of points x, the following should be true:

        .. code-block:: python

            y1 = t1.compose(t2, t3).transform(x)
            y2 = t3.transform(t2.transform(t1.transform(x)))
            y1.get_matrix() == y2.get_matrix()

        Returns:
            A transformation matrix representing the composed inputs.
        """
        composed_matrix = self._matrix.clone()
        if len(self._transforms) > 0:
            for other in self._transforms:
                other_matrix = other.get_matrix()
                composed_matrix = _broadcast_bmm(composed_matrix, other_matrix)
        return composed_matrix

    def _get_matrix_inverse(self):
        """
        Return the inverse of self._matrix.
        """
        return torch.inverse(self._matrix)

    def inverse(self, invert_composed: bool = False):
        """
        Returns a new Transform3D object that represents an inverse of the
        current transformation.

        Args:
            invert_composed:
                - True: First compose the list of stored transformations
                  and then apply inverse to the result. This is
                  potentially slower for classes of transformations
                  with inverses that can be computed efficiently
                  (e.g. rotations and translations).
                - False: Invert the individual stored transformations
                  independently without composing them.

        Returns:
            A new Transform3D object contaning the inverse of the original
            transformation.
        """

        tinv = Transform3d(device=self.device)

        if invert_composed:
            # first compose then invert
            tinv._matrix = torch.inverse(self.get_matrix())
        else:
            # self._get_matrix_inverse() implements efficient inverse
            # of self._matrix
            i_matrix = self._get_matrix_inverse()

            # 2 cases:
            if len(self._transforms) > 0:
                # a) Either we have a non-empty list of transforms:
                # Here we take self._matrix and append its inverse at the
                # end of the reverted _transforms list. After composing
                # the transformations with get_matrix(), this correctly
                # right-multiplies by the inverse of self._matrix
                # at the end of the composition.
                tinv._transforms = [
                    t.inverse() for t in reversed(self._transforms)
                ]
                last = Transform3d(device=self.device)
                last._matrix = i_matrix
                tinv._transforms.append(last)
            else:
                # b) Or there are no stored transformations
                # we just set inverted matrix
                tinv._matrix = i_matrix

        return tinv

    def stack(self, *others):
        transforms = [self] + list(others)
        matrix = torch.cat([t._matrix for t in transforms], dim=0)
        out = Transform3d()
        out._matrix = matrix
        return out

    def transform_points(self, points, eps: float = None):
        """
        Use this transform to transform a set of 3D points. Assumes row major
        ordering of the input points.

        Args:
            points: Tensor of shape (P, 3) or (N, P, 3)
            eps: If eps!=None, the argument is used to clamp the
                last coordinate before peforming the final division.
                The clamping corresponds to:
                last_coord := (last_coord.sign() + (last_coord==0)) *
                torch.clamp(last_coord.abs(), eps),
                i.e. the last coordinates that are exactly 0 will
                be clamped to +eps.

        Returns:
            points_out: points of shape (N, P, 3) or (P, 3) depending
            on the dimensions of the transform
        """
        points_batch = points.clone()
        if points_batch.dim() == 2:
            points_batch = points_batch[None]  # (P, 3) -> (1, P, 3)
        if points_batch.dim() != 3:
            msg = "Expected points to have dim = 2 or dim = 3: got shape %r"
            raise ValueError(msg % points.shape)

        N, P, _3 = points_batch.shape
        ones = torch.ones(N, P, 1, dtype=points.dtype, device=points.device)
        points_batch = torch.cat([points_batch, ones], dim=2)

        composed_matrix = self.get_matrix()
        points_out = _broadcast_bmm(points_batch, composed_matrix)
        denom = points_out[..., 3:]  # denominator
        if eps is not None:
            denom_sign = denom.sign() + (denom == 0.0).type_as(denom)
            denom = denom_sign * torch.clamp(denom.abs(), eps)
        points_out = points_out[..., :3] / denom

        # When transform is (1, 4, 4) and points is (P, 3) return
        # points_out of shape (P, 3)
        if points_out.shape[0] == 1 and points.dim() == 2:
            points_out = points_out.reshape(points.shape)

        return points_out

    def transform_normals(self, normals):
        """
        Use this transform to transform a set of normal vectors.

        Args:
            normals: Tensor of shape (P, 3) or (N, P, 3)

        Returns:
            normals_out: Tensor of shape (P, 3) or (N, P, 3) depending
            on the dimensions of the transform
        """
        if normals.dim() not in [2, 3]:
            msg = "Expected normals to have dim = 2 or dim = 3: got shape %r"
            raise ValueError(msg % normals.shape)
        composed_matrix = self.get_matrix()

        # TODO: inverse is bad! Solve a linear system instead
        mat = composed_matrix[:, :3, :3]
        normals_out = _broadcast_bmm(normals, mat.transpose(1, 2).inverse())

        # This doesn't pass unit tests. TODO investigate further
        # if self._lu is None:
        #     self._lu = self._matrix[:, :3, :3].transpose(1, 2).lu()
        # normals_out = normals.lu_solve(*self._lu)

        # When transform is (1, 4, 4) and normals is (P, 3) return
        # normals_out of shape (P, 3)
        if normals_out.shape[0] == 1 and normals.dim() == 2:
            normals_out = normals_out.reshape(normals.shape)

        return normals_out

    def translate(self, *args, **kwargs):
        return self.compose(Translate(device=self.device, *args, **kwargs))

    def scale(self, *args, **kwargs):
        return self.compose(Scale(device=self.device, *args, **kwargs))

    def rotate_axis_angle(self, *args, **kwargs):
        return self.compose(
            RotateAxisAngle(device=self.device, *args, **kwargs)
        )

    def clone(self):
        """
        Deep copy of Transforms object. All internal tensors are cloned
        individually.

        Returns:
            new Transforms object.
        """
        other = Transform3d(device=self.device)
        if self._lu is not None:
            other._lu = [l.clone() for l in self._lu]
        other._matrix = self._matrix.clone()
        other._transforms = [t.clone() for t in self._transforms]
        return other

    def to(self, device, copy: bool = False, dtype=None):
        """
        Match functionality of torch.Tensor.to()
        If copy = True or the self Tensor is on a different device, the
        returned tensor is a copy of self with the desired torch.device.
        If copy = False and the self Tensor already has the correct torch.device,
        then self is returned.

        Args:
          device: Device id for the new tensor.
          copy: Boolean indicator whether or not to clone self. Default False.
          dtype: If not None, casts the internal tensor variables
              to a given torch.dtype.

        Returns:
          Transform3d object.
        """
        if not copy and self.device == device:
            return self
        other = self.clone()
        if self.device != device:
            other.device = device
            other._matrix = self._matrix.to(device=device, dtype=dtype)
            for t in other._transforms:
                t.to(device, copy=copy, dtype=dtype)
        return other

    def cpu(self):
        return self.to(torch.device("cpu"))

    def cuda(self):
        return self.to(torch.device("cuda"))


class Translate(Transform3d):
    def __init__(
        self, x, y=None, z=None, dtype=torch.float32, device: str = "cpu"
    ):
        """
        Create a new Transform3d representing 3D translations.

        Option I: Translate(xyz, dtype=torch.float32, device='cpu')
            xyz should be a tensor of shape (N, 3)

        Option II: Translate(x, y, z, dtype=torch.float32, device='cpu')
            Here x, y, and z will be broadcast against each other and
            concatenated to form the translation. Each can be:
                - A python scalar
                - A torch scalar
                - A 1D torch tensor
        """
        super().__init__(device=device)
        xyz = _handle_input(x, y, z, dtype, device, "Translate")
        N = xyz.shape[0]

        mat = torch.eye(4, dtype=dtype, device=device)
        mat = mat.view(1, 4, 4).repeat(N, 1, 1)
        mat[:, 3, :3] = xyz
        self._matrix = mat

    def _get_matrix_inverse(self):
        """
        Return the inverse of self._matrix.
        """
        inv_mask = self._matrix.new_ones([1, 4, 4])
        inv_mask[0, 3, :3] = -1.0
        i_matrix = self._matrix * inv_mask
        return i_matrix


class Scale(Transform3d):
    def __init__(
        self, x, y=None, z=None, dtype=torch.float32, device: str = "cpu"
    ):
        """
        A Transform3d representing a scaling operation, with different scale
        factors along each coordinate axis.

        Option I: Scale(s, dtype=torch.float32, device='cpu')
            s can be one of
                - Python scalar or torch scalar: Single uniform scale
                - 1D torch tensor of shape (N,): A batch of uniform scale
                - 2D torch tensor of shape (N, 3): Scale differently along each axis

        Option II: Scale(x, y, z, dtype=torch.float32, device='cpu')
            Each of x, y, and z can be one of
                - python scalar
                - torch scalar
                - 1D torch tensor
        """
        super().__init__(device=device)
        xyz = _handle_input(
            x, y, z, dtype, device, "scale", allow_singleton=True
        )
        N = xyz.shape[0]

        # TODO: Can we do this all in one go somehow?
        mat = torch.eye(4, dtype=dtype, device=device)
        mat = mat.view(1, 4, 4).repeat(N, 1, 1)
        mat[:, 0, 0] = xyz[:, 0]
        mat[:, 1, 1] = xyz[:, 1]
        mat[:, 2, 2] = xyz[:, 2]
        self._matrix = mat

    def _get_matrix_inverse(self):
        """
        Return the inverse of self._matrix.
        """
        xyz = torch.stack([self._matrix[:, i, i] for i in range(4)], dim=1)
        ixyz = 1.0 / xyz
        imat = torch.diag_embed(ixyz, dim1=1, dim2=2)
        return imat


class Rotate(Transform3d):
    def __init__(
        self,
        R,
        dtype=torch.float32,
        device: str = "cpu",
        orthogonal_tol: float = 1e-5,
    ):
        """
        Create a new Transform3d representing 3D rotation using a rotation
        matrix as the input.

        Args:
            R: a tensor of shape (3, 3) or (N, 3, 3)
            orthogonal_tol: tolerance for the test of the orthogonality of R

        """
        super().__init__(device=device)
        if R.dim() == 2:
            R = R[None]
        if R.shape[-2:] != (3, 3):
            msg = "R must have shape (3, 3) or (N, 3, 3); got %s"
            raise ValueError(msg % repr(R.shape))
        R = R.to(dtype=dtype).to(device=device)
        _check_valid_rotation_matrix(R, tol=orthogonal_tol)
        N = R.shape[0]
        mat = torch.eye(4, dtype=dtype, device=device)
        mat = mat.view(1, 4, 4).repeat(N, 1, 1)
        mat[:, :3, :3] = R
        self._matrix = mat

    def _get_matrix_inverse(self):
        """
        Return the inverse of self._matrix.
        """
        return self._matrix.permute(0, 2, 1).contiguous()


class RotateAxisAngle(Rotate):
    def __init__(
        self,
        angle,
        axis: str = "X",
        degrees: bool = True,
        dtype=torch.float64,
        device: str = "cpu",
    ):
        """
        Create a new Transform3d representing 3D rotation about an axis
        by an angle.

        Args:
            angle:
                - A torch tensor of shape (N, 1)
                - A python scalar
                - A torch scalar
            axis:
                string: one of ["X", "Y", "Z"] indicating the axis about which
                to rotate.
                NOTE: All batch elements are rotated about the same axis.
        """
        axis = axis.upper()
        if axis not in ["X", "Y", "Z"]:
            msg = "Expected axis to be one of ['X', 'Y', 'Z']; got %s"
            raise ValueError(msg % axis)
        angle = _handle_angle_input(angle, dtype, device, "RotateAxisAngle")
        angle = (angle / 180.0 * math.pi) if degrees else angle
        N = angle.shape[0]

        cos = torch.cos(angle)
        sin = torch.sin(angle)
        one = torch.ones_like(angle)
        zero = torch.zeros_like(angle)

        if axis == "X":
            R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos)
        if axis == "Y":
            R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos)
        if axis == "Z":
            R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one)

        R = torch.stack(R_flat, -1).reshape((N, 3, 3))
        super().__init__(device=device, R=R)


def _handle_coord(c, dtype, device):
    """
    Helper function for _handle_input.

    Args:
        c: Python scalar, torch scalar, or 1D torch tensor

    Returns:
        c_vec: 1D torch tensor
    """
    if not torch.is_tensor(c):
        c = torch.tensor(c, dtype=dtype, device=device)
    if c.dim() == 0:
        c = c.view(1)
    return c


def _handle_input(
    x, y, z, dtype, device, name: str, allow_singleton: bool = False
):
    """
    Helper function to handle parsing logic for building transforms. The output
    is always a tensor of shape (N, 3), but there are several types of allowed
    input.

    Case I: Single Matrix
        In this case x is a tensor of shape (N, 3), and y and z are None. Here just
        return x.

    Case II: Vectors and Scalars
        In this case each of x, y, and z can be one of the following
            - Python scalar
            - Torch scalar
            - Torch tensor of shape (N, 1) or (1, 1)
        In this case x, y and z are broadcast to tensors of shape (N, 1)
        and concatenated to a tensor of shape (N, 3)

    Case III: Singleton (only if allow_singleton=True)
        In this case y and z are None, and x can be one of the following:
            - Python scalar
            - Torch scalar
            - Torch tensor of shape (N, 1) or (1, 1)
        Here x will be duplicated 3 times, and we return a tensor of shape (N, 3)

    Returns:
        xyz: Tensor of shape (N, 3)
    """
    # If x is actually a tensor of shape (N, 3) then just return it
    if torch.is_tensor(x) and x.dim() == 2:
        if x.shape[1] != 3:
            msg = "Expected tensor of shape (N, 3); got %r (in %s)"
            raise ValueError(msg % (x.shape, name))
        if y is not None or z is not None:
            msg = "Expected y and z to be None (in %s)" % name
            raise ValueError(msg)
        return x

    if allow_singleton and y is None and z is None:
        y = x
        z = x

    # Convert all to 1D tensors
    xyz = [_handle_coord(c, dtype, device) for c in [x, y, z]]

    # Broadcast and concatenate
    sizes = [c.shape[0] for c in xyz]
    N = max(sizes)
    for c in xyz:
        if c.shape[0] != 1 and c.shape[0] != N:
            msg = "Got non-broadcastable sizes %r (in %s)" % (sizes, name)
            raise ValueError(msg)
    xyz = [c.expand(N) for c in xyz]
    xyz = torch.stack(xyz, dim=1)
    return xyz


def _handle_angle_input(x, dtype, device: str, name: str):
    """
    Helper function for building a rotation function using angles.
    The output is always of shape (N, 1).

    The input can be one of:
        - Torch tensor (N, 1) or (N)
        - Python scalar
        - Torch scalar
    """
    # If x is actually a tensor of shape (N, 1) then just return it
    if torch.is_tensor(x) and x.dim() == 2:
        if x.shape[1] != 1:
            msg = "Expected tensor of shape (N, 1); got %r (in %s)"
            raise ValueError(msg % (x.shape, name))
        return x
    else:
        return _handle_coord(x, dtype, device)


def _broadcast_bmm(a, b):
    """
    Batch multiply two matrices and broadcast if necessary.

    Args:
        a: torch tensor of shape (P, K) or (M, P, K)
        b: torch tensor of shape (N, K, K)

    Returns:
        a and b broadcast multipled. The output batch dimension is max(N, M).

    To broadcast transforms across a batch dimension if M != N then
    expect that either M = 1 or N = 1. The tensor with batch dimension 1 is
    expanded to have shape N or M.
    """
    if a.dim() == 2:
        a = a[None]
    if len(a) != len(b):
        if not ((len(a) == 1) or (len(b) == 1)):
            msg = "Expected batch dim for bmm to be equal or 1; got %r, %r"
            raise ValueError(msg % (a.shape, b.shape))
        if len(a) == 1:
            a = a.expand(len(b), -1, -1)
        if len(b) == 1:
            b = b.expand(len(a), -1, -1)
    return a.bmm(b)


def _check_valid_rotation_matrix(R, tol: float = 1e-7):
    """
    Determine if R is a valid rotation matrix by checking it satisfies the
    following conditions:

    ``RR^T = I and det(R) = 1``

    Args:
        R: an (N, 3, 3) matrix

    Returns:
        None

    Emits a warning if R is an invalid rotation matrix.
    """
    N = R.shape[0]
    eye = torch.eye(3, dtype=R.dtype, device=R.device)
    eye = eye.view(1, 3, 3).expand(N, -1, -1)
    orthogonal = torch.allclose(R.bmm(R.transpose(1, 2)), eye, atol=tol)
    det_R = torch.det(R)
    no_distortion = torch.allclose(det_R, torch.ones_like(det_R))
    if not (orthogonal and no_distortion):
        msg = "R is not a valid rotation matrix"
        warnings.warn(msg)
    return
