import math
from collections import defaultdict

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from igl import fast_winding_number_for_meshes, point_mesh_squared_distance, read_obj
from torch.autograd import Function
from torch.cuda.amp import custom_bwd, custom_fwd

import threestudio
from threestudio.utils.typing import *


def dot(x, y):
    return torch.sum(x * y, -1, keepdim=True)


def reflect(x, n):
    return 2 * dot(x, n) * n - x


ValidScale = Union[Tuple[float, float], Num[Tensor, "2 D"]]


def scale_tensor(
    dat: Num[Tensor, "... D"], inp_scale: ValidScale, tgt_scale: ValidScale
):
    if inp_scale is None:
        inp_scale = (0, 1)
    if tgt_scale is None:
        tgt_scale = (0, 1)
    if isinstance(tgt_scale, Tensor):
        assert dat.shape[-1] == tgt_scale.shape[-1]
    dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])
    dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]
    return dat


class _TruncExp(Function):  # pylint: disable=abstract-method
    # Implementation from torch-ngp:
    # https://github.com/ashawkey/torch-ngp/blob/93b08a0d4ec1cc6e69d85df7f0acdfb99603b628/activation.py
    @staticmethod
    @custom_fwd(cast_inputs=torch.float32)
    def forward(ctx, x):  # pylint: disable=arguments-differ
        ctx.save_for_backward(x)
        return torch.exp(x)

    @staticmethod
    @custom_bwd
    def backward(ctx, g):  # pylint: disable=arguments-differ
        x = ctx.saved_tensors[0]
        return g * torch.exp(torch.clamp(x, max=15))


class SpecifyGradient(Function):
    # Implementation from stable-dreamfusion
    # https://github.com/ashawkey/stable-dreamfusion
    @staticmethod
    @custom_fwd
    def forward(ctx, input_tensor, gt_grad):
        ctx.save_for_backward(gt_grad)
        # we return a dummy value 1, which will be scaled by amp's scaler so we get the scale in backward.
        return torch.ones([1], device=input_tensor.device, dtype=input_tensor.dtype)

    @staticmethod
    @custom_bwd
    def backward(ctx, grad_scale):
        (gt_grad,) = ctx.saved_tensors
        gt_grad = gt_grad * grad_scale
        return gt_grad, None


trunc_exp = _TruncExp.apply


def get_activation(name) -> Callable:
    if name is None:
        return lambda x: x
    name = name.lower()
    if name == "none":
        return lambda x: x
    elif name == "lin2srgb":
        return lambda x: torch.where(
            x > 0.0031308,
            torch.pow(torch.clamp(x, min=0.0031308), 1.0 / 2.4) * 1.055 - 0.055,
            12.92 * x,
        ).clamp(0.0, 1.0)
    elif name == "exp":
        return lambda x: torch.exp(x)
    elif name == "shifted_exp":
        return lambda x: torch.exp(x - 1.0)
    elif name == "trunc_exp":
        return trunc_exp
    elif name == "shifted_trunc_exp":
        return lambda x: trunc_exp(x - 1.0)
    elif name == "sigmoid":
        return lambda x: torch.sigmoid(x)
    elif name == "tanh":
        return lambda x: torch.tanh(x)
    elif name == "shifted_softplus":
        return lambda x: F.softplus(x - 1.0)
    elif name == "scale_-11_01":
        return lambda x: x * 0.5 + 0.5
    else:
        try:
            return getattr(F, name)
        except AttributeError:
            raise ValueError(f"Unknown activation function: {name}")


def chunk_batch(func: Callable, chunk_size: int, *args, **kwargs) -> Any:
    if chunk_size <= 0:
        return func(*args, **kwargs)
    B = None
    for arg in list(args) + list(kwargs.values()):
        if isinstance(arg, torch.Tensor):
            B = arg.shape[0]
            break
    assert (
        B is not None
    ), "No tensor found in args or kwargs, cannot determine batch size."
    out = defaultdict(list)
    out_type = None
    # max(1, B) to support B == 0
    for i in range(0, max(1, B), chunk_size):
        out_chunk = func(
            *[
                arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg
                for arg in args
            ],
            **{
                k: arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg
                for k, arg in kwargs.items()
            },
        )
        if out_chunk is None:
            continue
        out_type = type(out_chunk)
        if isinstance(out_chunk, torch.Tensor):
            out_chunk = {0: out_chunk}
        elif isinstance(out_chunk, tuple) or isinstance(out_chunk, list):
            chunk_length = len(out_chunk)
            out_chunk = {i: chunk for i, chunk in enumerate(out_chunk)}
        elif isinstance(out_chunk, dict):
            pass
        else:
            print(
                f"Return value of func must be in type [torch.Tensor, list, tuple, dict], get {type(out_chunk)}."
            )
            exit(1)
        for k, v in out_chunk.items():
            v = v if torch.is_grad_enabled() else v.detach()
            out[k].append(v)

    if out_type is None:
        return None

    out_merged: Dict[Any, Optional[torch.Tensor]] = {}
    for k, v in out.items():
        if all([vv is None for vv in v]):
            # allow None in return value
            out_merged[k] = None
        elif all([isinstance(vv, torch.Tensor) for vv in v]):
            out_merged[k] = torch.cat(v, dim=0)
        else:
            raise TypeError(
                f"Unsupported types in return value of func: {[type(vv) for vv in v if not isinstance(vv, torch.Tensor)]}"
            )

    if out_type is torch.Tensor:
        return out_merged[0]
    elif out_type in [tuple, list]:
        return out_type([out_merged[i] for i in range(chunk_length)])
    elif out_type is dict:
        return out_merged


def get_ray_directions(
    H: int,
    W: int,
    focal: Union[float, Tuple[float, float]],
    principal: Optional[Tuple[float, float]] = None,
    use_pixel_centers: bool = True,
) -> Float[Tensor, "H W 3"]:
    """
    Get ray directions for all pixels in camera coordinate.
    Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/
               ray-tracing-generating-camera-rays/standard-coordinate-systems

    Inputs:
        H, W, focal, principal, use_pixel_centers: image height, width, focal length, principal point and whether use pixel centers
    Outputs:
        directions: (H, W, 3), the direction of the rays in camera coordinate
    """
    pixel_center = 0.5 if use_pixel_centers else 0

    if isinstance(focal, float):
        fx, fy = focal, focal
        cx, cy = W / 2, H / 2
    else:
        fx, fy = focal
        assert principal is not None
        cx, cy = principal

    i, j = torch.meshgrid(
        torch.arange(W, dtype=torch.float32) + pixel_center,
        torch.arange(H, dtype=torch.float32) + pixel_center,
        indexing="xy",
    )

    directions: Float[Tensor, "H W 3"] = torch.stack(
        [(i - cx) / fx, -(j - cy) / fy, -torch.ones_like(i)], -1
    )

    return directions


def get_rays(
    directions: Float[Tensor, "... 3"],
    c2w: Float[Tensor, "... 4 4"],
    keepdim=False,
    noise_scale=0.0,
    normalize=True,
) -> Tuple[Float[Tensor, "... 3"], Float[Tensor, "... 3"]]:
    # Rotate ray directions from camera coordinate to the world coordinate
    assert directions.shape[-1] == 3

    if directions.ndim == 2:  # (N_rays, 3)
        if c2w.ndim == 2:  # (4, 4)
            c2w = c2w[None, :, :]
        assert c2w.ndim == 3  # (N_rays, 4, 4) or (1, 4, 4)
        rays_d = (directions[:, None, :] * c2w[:, :3, :3]).sum(-1)  # (N_rays, 3)
        rays_o = c2w[:, :3, 3].expand(rays_d.shape)
    
    elif directions.ndim == 3:  # (H, W, 3)
        assert c2w.ndim in [2, 3]
        if c2w.ndim == 2:  # (4, 4)
            rays_d = (directions[:, :, None, :] * c2w[None, None, :3, :3]).sum(
                -1
            )  # (H, W, 3)
            rays_o = c2w[None, None, :3, 3].expand(rays_d.shape)
        elif c2w.ndim == 3:  # (B, 4, 4)
            rays_d = (directions[None, :, :, None, :] * c2w[:, None, None, :3, :3]).sum(
                -1
            )  # (B, H, W, 3)
            rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape)
    
    elif directions.ndim == 4:  # (B, H, W, 3)
        assert c2w.ndim == 3  # (B, 4, 4)
        rays_d = (directions[:, :, :, None, :] * c2w[:, None, None, :3, :3]).sum(
            -1
        )  # (B, H, W, 3)
        rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape)

    # add camera noise to avoid grid-like artifect
    # https://github.com/ashawkey/stable-dreamfusion/blob/49c3d4fa01d68a4f027755acf94e1ff6020458cc/nerf/utils.py#L373
    if noise_scale > 0:
        rays_o = rays_o + torch.randn(3, device=rays_o.device) * noise_scale
        rays_d = rays_d + torch.randn(3, device=rays_d.device) * noise_scale

    if normalize:
        rays_d = F.normalize(rays_d, dim=-1)
    if not keepdim:
        rays_o, rays_d = rays_o.reshape(-1, 3), rays_d.reshape(-1, 3)

    return rays_o, rays_d


def get_projection_matrix(
    fovy: Float[Tensor, "B"], aspect_wh: float, near: float, far: float
) -> Float[Tensor, "B 4 4"]:
    batch_size = fovy.shape[0]
    proj_mtx = torch.zeros(batch_size, 4, 4, dtype=torch.float32)
    proj_mtx[:, 0, 0] = 1.0 / (torch.tan(fovy / 2.0) * aspect_wh)
    proj_mtx[:, 1, 1] = -1.0 / torch.tan(
        fovy / 2.0
    )  # add a negative sign here as the y axis is flipped in nvdiffrast output
    proj_mtx[:, 2, 2] = -(far + near) / (far - near)
    proj_mtx[:, 2, 3] = -2.0 * far * near / (far - near)
    proj_mtx[:, 3, 2] = -1.0
    return proj_mtx


def get_mvp_matrix(
    c2w: Float[Tensor, "B 4 4"], proj_mtx: Float[Tensor, "B 4 4"]
) -> Float[Tensor, "B 4 4"]:
    # calculate w2c from c2w: R' = Rt, t' = -Rt * t
    # mathematically equivalent to (c2w)^-1
    w2c: Float[Tensor, "B 4 4"] = torch.zeros(c2w.shape[0], 4, 4).to(c2w)
    w2c[:, :3, :3] = c2w[:, :3, :3].permute(0, 2, 1)
    w2c[:, :3, 3:] = -c2w[:, :3, :3].permute(0, 2, 1) @ c2w[:, :3, 3:]
    w2c[:, 3, 3] = 1.0
    # calculate mvp matrix by proj_mtx @ w2c (mv_mtx)
    mvp_mtx = proj_mtx @ w2c
    return mvp_mtx


def get_full_projection_matrix(
    c2w: Float[Tensor, "B 4 4"], proj_mtx: Float[Tensor, "B 4 4"]
) -> Float[Tensor, "B 4 4"]:
    return (c2w.unsqueeze(0).bmm(proj_mtx.unsqueeze(0))).squeeze(0)


def binary_cross_entropy(input, target):
    """
    F.binary_cross_entropy is not numerically stable in mixed-precision training.
    """
    return -(target * torch.log(input) + (1 - target) * torch.log(1 - input)).mean()


def tet_sdf_diff(
    vert_sdf: Float[Tensor, "Nv 1"], tet_edges: Integer[Tensor, "Ne 2"]
) -> Float[Tensor, ""]:
    sdf_f1x6x2 = vert_sdf[:, 0][tet_edges.reshape(-1)].reshape(-1, 2)
    mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1])
    sdf_f1x6x2 = sdf_f1x6x2[mask]
    sdf_diff = F.binary_cross_entropy_with_logits(
        sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float()
    ) + F.binary_cross_entropy_with_logits(
        sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float()
    )
    return sdf_diff


# Implementation from Latent-NeRF
# https://github.com/eladrich/latent-nerf/blob/f49ecefcd48972e69a28e3116fe95edf0fac4dc8/src/latent_nerf/models/mesh_utils.py
class MeshOBJ:
    dx = torch.zeros(3).float()
    dx[0] = 1
    dy, dz = dx[[1, 0, 2]], dx[[2, 1, 0]]
    dx, dy, dz = dx[None, :], dy[None, :], dz[None, :]

    def __init__(self, v: np.ndarray, f: np.ndarray):
        self.v = v
        self.f = f
        self.dx, self.dy, self.dz = MeshOBJ.dx, MeshOBJ.dy, MeshOBJ.dz
        self.v_tensor = torch.from_numpy(self.v)

        vf = self.v[self.f, :]
        self.f_center = vf.mean(axis=1)
        self.f_center_tensor = torch.from_numpy(self.f_center).float()

        e1 = vf[:, 1, :] - vf[:, 0, :]
        e2 = vf[:, 2, :] - vf[:, 0, :]
        self.face_normals = np.cross(e1, e2)
        self.face_normals = (
            self.face_normals / np.linalg.norm(self.face_normals, axis=-1)[:, None]
        )
        self.face_normals_tensor = torch.from_numpy(self.face_normals)

    def normalize_mesh(self, target_scale=0.5):
        verts = self.v

        # Compute center of bounding box
        # center = torch.mean(torch.column_stack([torch.max(verts, dim=0)[0], torch.min(verts, dim=0)[0]]))
        center = verts.mean(axis=0)
        verts = verts - center
        scale = np.max(np.linalg.norm(verts, axis=1))
        verts = (verts / scale) * target_scale

        return MeshOBJ(verts, self.f)

    def winding_number(self, query: torch.Tensor):
        device = query.device
        shp = query.shape
        query_np = query.detach().cpu().reshape(-1, 3).numpy()
        target_alphas = fast_winding_number_for_meshes(
            self.v.astype(np.float32), self.f, query_np
        )
        return torch.from_numpy(target_alphas).reshape(shp[:-1]).to(device)

    def gaussian_weighted_distance(self, query: torch.Tensor, sigma):
        device = query.device
        shp = query.shape
        query_np = query.detach().cpu().reshape(-1, 3).numpy()
        distances, _, _ = point_mesh_squared_distance(
            query_np, self.v.astype(np.float32), self.f
        )
        distances = torch.from_numpy(distances).reshape(shp[:-1]).to(device)
        weight = torch.exp(-(distances / (2 * sigma**2)))
        return weight


def ce_pq_loss(p, q, weight=None):
    def clamp(v, T=0.0001):
        return v.clamp(T, 1 - T)

    p = p.view(q.shape)
    ce = -1 * (p * torch.log(clamp(q)) + (1 - p) * torch.log(clamp(1 - q)))
    if weight is not None:
        ce *= weight
    return ce.sum()


class ShapeLoss(nn.Module):
    def __init__(self, guide_shape):
        super().__init__()
        self.mesh_scale = 0.7
        self.proximal_surface = 0.3
        self.delta = 0.2
        self.shape_path = guide_shape
        v, _, _, f, _, _ = read_obj(self.shape_path, float)
        mesh = MeshOBJ(v, f)
        matrix_rot = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) @ np.array(
            [[0, 0, 1], [0, 1, 0], [-1, 0, 0]]
        )
        self.sketchshape = mesh.normalize_mesh(self.mesh_scale)
        self.sketchshape = MeshOBJ(
            np.ascontiguousarray(
                (matrix_rot @ self.sketchshape.v.transpose(1, 0)).transpose(1, 0)
            ),
            f,
        )

    def forward(self, xyzs, sigmas):
        mesh_occ = self.sketchshape.winding_number(xyzs)
        if self.proximal_surface > 0:
            weight = 1 - self.sketchshape.gaussian_weighted_distance(
                xyzs, self.proximal_surface
            )
        else:
            weight = None
        indicator = (mesh_occ > 0.5).float()
        nerf_occ = 1 - torch.exp(-self.delta * sigmas)
        nerf_occ = nerf_occ.clamp(min=0, max=1.1)
        loss = ce_pq_loss(
            nerf_occ, indicator, weight=weight
        )  # order is important for CE loss + second argument may not be optimized
        return loss


def shifted_expotional_decay(a, b, c, r):
    return a * torch.exp(-b * r) + c


def shifted_cosine_decay(a, b, c, r):
    return a * torch.cos(b * r + c) + a


def perpendicular_component(x: Float[Tensor, "B C H W"], y: Float[Tensor, "B C H W"]):
    # get the component of x that is perpendicular to y
    eps = torch.ones_like(x[:, 0, 0, 0]) * 1e-6
    return (
        x
        - (
            torch.mul(x, y).sum(dim=[1, 2, 3])
            / torch.maximum(torch.mul(y, y).sum(dim=[1, 2, 3]), eps)
        ).view(-1, 1, 1, 1)
        * y
    )


def validate_empty_rays(ray_indices, t_start, t_end):
    if ray_indices.nelement() == 0:
        threestudio.warn("Empty rays_indices!")
        ray_indices = torch.LongTensor([0]).to(ray_indices)
        t_start = torch.Tensor([0]).to(ray_indices)
        t_end = torch.Tensor([0]).to(ray_indices)
    return ray_indices, t_start, t_end