# flake8: noqa: F722
# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from types import SimpleNamespace
from typing import Optional
import numpy as np
import torch
import torch.nn.functional as F
from einops import einsum


def as_homogeneous(ext):
    """
    Accept (..., 3,4) or (..., 4,4) extrinsics, return (...,4,4) homogeneous matrix.
    Supports torch.Tensor or np.ndarray.
    """
    if isinstance(ext, torch.Tensor):
        # If already in homogeneous form
        if ext.shape[-2:] == (4, 4):
            return ext
        elif ext.shape[-2:] == (3, 4):
            # Create a new homogeneous matrix
            ones = torch.zeros_like(ext[..., :1, :4])
            ones[..., 0, 3] = 1.0
            return torch.cat([ext, ones], dim=-2)
        else:
            raise ValueError(f"Invalid shape for torch.Tensor: {ext.shape}")

    elif isinstance(ext, np.ndarray):
        if ext.shape[-2:] == (4, 4):
            return ext
        elif ext.shape[-2:] == (3, 4):
            ones = np.zeros_like(ext[..., :1, :4])
            ones[..., 0, 3] = 1.0
            return np.concatenate([ext, ones], axis=-2)
        else:
            raise ValueError(f"Invalid shape for np.ndarray: {ext.shape}")

    else:
        raise TypeError("Input must be a torch.Tensor or np.ndarray.")


@torch.jit.script
def affine_inverse(A: torch.Tensor):
    R = A[..., :3, :3]  # ..., 3, 3
    T = A[..., :3, 3:]  # ..., 3, 1
    P = A[..., 3:, :]  # ..., 1, 4
    return torch.cat([torch.cat([R.mT, -R.mT @ T], dim=-1), P], dim=-2)


def transpose_last_two_axes(arr):
    """
    for np < 2
    """
    if arr.ndim < 2:
        return arr
    axes = list(range(arr.ndim))
    # swap the last two
    axes[-2], axes[-1] = axes[-1], axes[-2]
    return arr.transpose(axes)


def affine_inverse_np(A: np.array):
    R = A[..., :3, :3]
    T = A[..., :3, 3:]
    P = A[..., 3:, :]
    return np.concatenate(
        [
            np.concatenate([transpose_last_two_axes(R), -transpose_last_two_axes(R) @ T], axis=-1),
            P,
        ],
        axis=-2,
    )


def quat_to_mat(quaternions: torch.Tensor) -> torch.Tensor:
    """
    Quaternion Order: XYZW or say ijkr, scalar-last

    Convert rotations given as quaternions to rotation matrices.
    Args:
        quaternions: quaternions with real part last,
            as tensor of shape (..., 4).

    Returns:
        Rotation matrices as tensor of shape (..., 3, 3).
    """
    i, j, k, r = torch.unbind(quaternions, -1)
    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
    two_s = 2.0 / (quaternions * quaternions).sum(-1)

    o = torch.stack(
        (
            1 - two_s * (j * j + k * k),
            two_s * (i * j - k * r),
            two_s * (i * k + j * r),
            two_s * (i * j + k * r),
            1 - two_s * (i * i + k * k),
            two_s * (j * k - i * r),
            two_s * (i * k - j * r),
            two_s * (j * k + i * r),
            1 - two_s * (i * i + j * j),
        ),
        -1,
    )
    return o.reshape(quaternions.shape[:-1] + (3, 3))


def mat_to_quat(matrix: torch.Tensor) -> torch.Tensor:
    """
    Convert rotations given as rotation matrices to quaternions.

    Args:
        matrix: Rotation matrices as tensor of shape (..., 3, 3).

    Returns:
        quaternions with real part last, as tensor of shape (..., 4).
        Quaternion Order: XYZW or say ijkr, scalar-last
    """
    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
        raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")

    batch_dim = matrix.shape[:-2]
    m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
        matrix.reshape(batch_dim + (9,)), dim=-1
    )

    q_abs = _sqrt_positive_part(
        torch.stack(
            [
                1.0 + m00 + m11 + m22,
                1.0 + m00 - m11 - m22,
                1.0 - m00 + m11 - m22,
                1.0 - m00 - m11 + m22,
            ],
            dim=-1,
        )
    )

    # we produce the desired quaternion multiplied by each of r, i, j, k
    quat_by_rijk = torch.stack(
        [
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
            #  `int`.
            torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
        ],
        dim=-2,
    )

    # We floor here at 0.1 but the exact level is not important; if q_abs is small,
    # the candidate won't be picked.
    flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))

    # if not for numerical problems, quat_candidates[i] should be same (up to a sign),
    # forall i; we pick the best-conditioned one (with the largest denominator)
    out = quat_candidates[F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :].reshape(
        batch_dim + (4,)
    )

    # Convert from rijk to ijkr
    out = out[..., [1, 2, 3, 0]]

    out = standardize_quaternion(out)

    return out


def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
    """
    Returns torch.sqrt(torch.max(0, x))
    but with a zero subgradient where x is 0.
    """
    ret = torch.zeros_like(x)
    positive_mask = x > 0
    if torch.is_grad_enabled():
        ret[positive_mask] = torch.sqrt(x[positive_mask])
    else:
        ret = torch.where(positive_mask, torch.sqrt(x), ret)
    return ret


def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor:
    """
    Convert a unit quaternion to a standard form: one in which the real
    part is non negative.

    Args:
        quaternions: Quaternions with real part last,
            as tensor of shape (..., 4).

    Returns:
        Standardized quaternions as tensor of shape (..., 4).
    """
    return torch.where(quaternions[..., 3:4] < 0, -quaternions, quaternions)


def sample_image_grid(
    shape: tuple[int, ...],
    device: torch.device = torch.device("cpu"),
) -> tuple[
    torch.Tensor,  # float coordinates (xy indexing), "*shape dim"
    torch.Tensor,  # integer indices (ij indexing), "*shape dim"
]:
    """Get normalized (range 0 to 1) coordinates and integer indices for an image."""

    # Each entry is a pixel-wise integer coordinate. In the 2D case, each entry is a
    # (row, col) coordinate.
    indices = [torch.arange(length, device=device) for length in shape]
    stacked_indices = torch.stack(torch.meshgrid(*indices, indexing="ij"), dim=-1)

    # Each entry is a floating-point coordinate in the range (0, 1). In the 2D case,
    # each entry is an (x, y) coordinate.
    coordinates = [(idx + 0.5) / length for idx, length in zip(indices, shape)]
    coordinates = reversed(coordinates)
    coordinates = torch.stack(torch.meshgrid(*coordinates, indexing="xy"), dim=-1)

    return coordinates, stacked_indices


def homogenize_points(points: torch.Tensor) -> torch.Tensor:  # "*batch dim"  # "*batch dim+1"
    """Convert batched points (xyz) to (xyz1)."""
    return torch.cat([points, torch.ones_like(points[..., :1])], dim=-1)


def homogenize_vectors(vectors: torch.Tensor) -> torch.Tensor:  #  "*batch dim"  # "*batch dim+1"
    """Convert batched vectors (xyz) to (xyz0)."""
    return torch.cat([vectors, torch.zeros_like(vectors[..., :1])], dim=-1)


def transform_rigid(
    homogeneous_coordinates: torch.Tensor,  # "*#batch dim"
    transformation: torch.Tensor,  # "*#batch dim dim"
) -> torch.Tensor:  # "*batch dim"
    """Apply a rigid-body transformation to points or vectors."""
    return einsum(
        transformation,
        homogeneous_coordinates.to(transformation.dtype),
        "... i j, ... j -> ... i",
    )


def transform_cam2world(
    homogeneous_coordinates: torch.Tensor,  # "*#batch dim"
    extrinsics: torch.Tensor,  # "*#batch dim dim"
) -> torch.Tensor:  # "*batch dim"
    """Transform points from 3D camera coordinates to 3D world coordinates."""
    return transform_rigid(homogeneous_coordinates, extrinsics)


def unproject(
    coordinates: torch.Tensor,  # "*#batch dim"
    z: torch.Tensor,  # "*#batch"
    intrinsics: torch.Tensor,  # "*#batch dim+1 dim+1"
) -> torch.Tensor:  # "*batch dim+1"
    """Unproject 2D camera coordinates with the given Z values."""

    # Apply the inverse intrinsics to the coordinates.
    coordinates = homogenize_points(coordinates)
    ray_directions = einsum(
        intrinsics.float().inverse().to(intrinsics),
        coordinates.to(intrinsics.dtype),
        "... i j, ... j -> ... i",
    )

    # Apply the supplied depth values.
    return ray_directions * z[..., None]


def get_world_rays(
    coordinates: torch.Tensor,  # "*#batch dim"
    extrinsics: torch.Tensor,  # "*#batch dim+2 dim+2"
    intrinsics: torch.Tensor,  # "*#batch dim+1 dim+1"
) -> tuple[
    torch.Tensor,  # origins, "*batch dim+1"
    torch.Tensor,  # directions, "*batch dim+1"
]:
    # Get camera-space ray directions.
    directions = unproject(
        coordinates,
        torch.ones_like(coordinates[..., 0]),
        intrinsics,
    )
    directions = directions / directions.norm(dim=-1, keepdim=True)

    # Transform ray directions to world coordinates.
    directions = homogenize_vectors(directions)
    directions = transform_cam2world(directions, extrinsics)[..., :-1]

    # Tile the ray origins to have the same shape as the ray directions.
    origins = extrinsics[..., :-1, -1].broadcast_to(directions.shape)

    return origins, directions


def get_fov(intrinsics: torch.Tensor) -> torch.Tensor:  # "batch 3 3" -> "batch 2"
    intrinsics_inv = intrinsics.float().inverse().to(intrinsics)

    def process_vector(vector):
        vector = torch.tensor(vector, dtype=intrinsics.dtype, device=intrinsics.device)
        vector = einsum(intrinsics_inv, vector, "b i j, j -> b i")
        return vector / vector.norm(dim=-1, keepdim=True)

    left = process_vector([0, 0.5, 1])
    right = process_vector([1, 0.5, 1])
    top = process_vector([0.5, 0, 1])
    bottom = process_vector([0.5, 1, 1])
    fov_x = (left * right).sum(dim=-1).acos()
    fov_y = (top * bottom).sum(dim=-1).acos()
    return torch.stack((fov_x, fov_y), dim=-1)


def map_pdf_to_opacity(
    pdf: torch.Tensor,  # " *batch"
    global_step: int = 0,
    opacity_mapping: Optional[dict] = None,
) -> torch.Tensor:  # " *batch"
    # https://www.desmos.com/calculator/opvwti3ba9

    # Figure out the exponent.
    if opacity_mapping is not None:
        cfg = SimpleNamespace(**opacity_mapping)
        x = cfg.initial + min(global_step / cfg.warm_up, 1) * (cfg.final - cfg.initial)
    else:
        x = 0.0
    exponent = 2**x

    # Map the probability density to an opacity.
    return 0.5 * (1 - (1 - pdf) ** exponent + pdf ** (1 / exponent))