from typing import Tuple
import torch
import torch.nn as nn
from .my import net_modules
from .my import util
from .my import device

rand_gen = torch.Generator(device=device.GetDevice())
rand_gen.manual_seed(torch.seed())


def RaySphereIntersect(p: torch.Tensor, v: torch.Tensor, r: torch.Tensor) -> torch.Tensor:
    """
    Calculate intersections of each rays and each spheres

    :param p ```Tensor(B, 3)```: positions of rays
    :param v ```Tensor(B, 3)```: directions of rays
    :param r ```Tensor(N)```: , radius of spheres
    :return ```Tensor(B, N, 3)```: points of intersection
    :return ```Tensor(B, N)```: depths of intersection along ray
    """
    # p, v: Expand to (B, 1, 3)
    p = p.unsqueeze(1)
    v = v.unsqueeze(1)
    # pp, vv, pv: (B, 1)
    pp = (p * p).sum(dim=2)
    vv = (v * v).sum(dim=2)
    pv = (p * v).sum(dim=2)
    depths = (((pv * pv - vv * (pp - r * r)).sqrt() - pv) / vv)
    return p + depths[..., None] * v, depths


class Rendering(nn.Module):

    def __init__(self, *, raw_noise_std: float = 0.0, white_bg: bool = False):
        """
        Initialize a Rendering module
        """
        super().__init__()
        self.raw_noise_std = raw_noise_std
        self.white_bg = white_bg

    def forward(self, raw, z_vals, ret_extra: bool = False):
        """Transforms model's predictions to semantically meaningful values.

        Args:
          raw: [num_rays, num_samples along ray, 4]. Prediction from model.
          z_vals: [num_rays, num_samples along ray]. Integration time.

        Returns:
          rgb_map: [num_rays, 3]. Estimated RGB color of a ray.
          disp_map: [num_rays]. Disparity map. Inverse of depth map.
          acc_map: [num_rays]. Sum of weights along each ray.
          weights: [num_rays, num_samples]. Weights assigned to each sampled color.
          depth_map: [num_rays]. Estimated distance to object.
        """
        # Function for computing density from model prediction. This value is
        # strictly between [0, 1].
        def raw2alpha(raw, dists, act_fn=torch.relu):
            return 1.0 - torch.exp(-act_fn(raw) * dists)

        # Compute 'distance' (in time) between each integration time along a ray.
        # The 'distance' from the last integration time is infinity.
        # dists: (N_rays, N_samples)
        dists = z_vals[..., 1:] - z_vals[..., :-1]
        dists = util.broadcast_cat(dists, 1e10)

        # Extract RGB of each sample position along each ray.
        color = torch.sigmoid(raw[..., :-1])  # (N_rays, N_samples, 1|3)

        # Add noise to model's predictions for density. Can be used to
        # regularize network during training (prevents floater artifacts).
        noise = 0.
        if self.raw_noise_std > 0.:
            noise = torch.normal(0.0, self.raw_noise_std,
                                 raw[..., 3].size(), rand_gen)

        # Predict density of each sample along each ray. Higher values imply
        # higher likelihood of being absorbed at this point.
        alpha = raw2alpha(raw[..., -1] + noise, dists)  # (N_rays, N_samples)

        # Compute weight for RGB of each sample along each ray.  A cumprod() is
        # used to express the idea of the ray not having reflected up to this
        # sample yet.
        one_minus_alpha = util.broadcast_cat(
            torch.cumprod(1 - alpha[..., :-1] + 1e-10, dim=-1),
            1.0, append=False)
        weights = alpha * one_minus_alpha  # (N_rays, N_samples)

        # (N_rays, 1|3), computed weighted color of each sample along each ray.
        color_map = torch.sum(weights[..., None] * color, dim=-2)

        # To composite onto a white background, use the accumulated alpha map.
        if self.white_bg or ret_extra:
             # Sum of weights along each ray. This value is in [0, 1] up to numerical error.
            acc_map = torch.sum(weights, -1)
            if self.white_bg:
                color_map = color_map + (1. - acc_map[..., None])
        else:
            acc_map = None

        if not ret_extra:
            return color_map
        
        # Estimated depth map is expected distance.
        depth_map = torch.sum(weights * z_vals, dim=-1)

        # Disparity map is inverse depth.
        disp_map = 1. / torch.max(1e-10, depth_map /
                                  torch.sum(weights, dim=-1))

        return color_map, disp_map, acc_map, weights, depth_map


class Sampler(nn.Module):

    def __init__(self, *, depth_range: Tuple[float, float], n_samples: int,
                 perturb_sample: bool, spherical: bool):
        """
        Initialize a Sampler module

        :param depth_range: depth range for sampler
        :param n_samples: count to sample along ray
        :param perturb_sample: perturb the sample depths
        """
        super().__init__()
        self.r = 1 / torch.linspace(1 / depth_range[0], 1 / depth_range[1],
                                    n_samples, device=device.GetDevice())
        self.perturb_sample = perturb_sample
        self.spherical = spherical
        if perturb_sample:
            mids = .5 * (self.r[1:] + self.r[:-1])
            self.upper = torch.cat([mids, self.r[-1:]], -1)
            self.lower = torch.cat([self.r[:1], mids], -1)

    def forward(self, rays_o, rays_d):
        """
        Sample points along rays. return Spherical or Cartesian coordinates, 
        specified by ```self.shperical```

        :param rays_o ```Tensor(B, 3)```: rays' origin
        :param rays_d ```Tensor(B, 3)```: rays' direction
        :return ```Tensor(B, N, 3)```: sampled points
        :return ```Tensor(B, N)```: corresponding depths along rays
        """
        if self.perturb_sample:
            # stratified samples in those intervals
            t_rand = torch.rand(self.r.size(),
                                generator=rand_gen,
                                device=device.GetDevice())
            r = self.lower + (self.upper - self.lower) * t_rand
        else:
            r = self.r

        if self.spherical:
            pts, depths = RaySphereIntersect(rays_o, rays_d, r)
            sphers = util.CartesianToSpherical(pts)
            sphers[..., 0] = 1 / sphers[..., 0]
            return sphers, depths
        else:
            return rays_o[..., None, :] + rays_d[..., None, :] * r[..., None], r


class MslNet(nn.Module):

    def __init__(self, fc_params, sampler_params,
                 gray=False,
                 encode_to_dim: int = 0):
        """
        Initialize a multi-sphere-layer net

        :param fc_params: parameters for full-connection network
        :param sampler_params: parameters for sampler
        :param gray: is grayscale mode
        :param encode_to_dim: encode input to number of dimensions
        """
        super().__init__()
        self.in_chns = 3
        self.input_encoder = net_modules.InputEncoder.Get(
            encode_to_dim, self.in_chns)
        fc_params['in_chns'] = self.input_encoder.out_dim
        fc_params['out_chns'] = 2 if gray else 4
        self.sampler = Sampler(**sampler_params)
        self.net = net_modules.FcNet(**fc_params)
        self.rendering = Rendering()

    def forward(self, rays_o: torch.Tensor, rays_d: torch.Tensor) -> torch.Tensor:
        """
        rays -> colors

        :param rays_o ```Tensor(B, ..., 3)```: rays' origin
        :param rays_d ```Tensor(B, ..., 3)```: rays' direction
        :return: Tensor(B, 1|3, ...), inferred images/pixels
        """
        p = rays_o.view(-1, 3)
        v = rays_d.view(-1, 3)
        coords, depths = self.sampler(p, v)
        encoded = self.input_encoder(coords)
        color_map = self.rendering(self.net(encoded), depths)
        
        # Unflatten according to input shape
        out_shape = list(rays_d.size())
        out_shape[-1] = -1
        return color_map.view(out_shape).movedim(-1, 1)