import os
import torch
import torch.nn.functional as nn_f
from typing import Tuple, Union
from utils import img
from utils import color
from utils import misc
from utils import sphere
from utils.mem_profiler import *
from utils.constants import *


class PanoDataset(object):
    """
    Data loader for spherical view synthesis task

    Attributes
    --------
    data_dir ```str```: the directory of dataset\n
    view_file_pattern ```str```: the filename pattern of view images\n
    cam_params ```object```: camera intrinsic parameters\n
    centers ```Tensor(N, 3)```: centers of views\n
    view_rots ```Tensor(N, 3, 3)```: rotation matrices of views\n
    images ```Tensor(N, 3, H, W)```: images of views\n
    view_depths ```Tensor(N, H, W)```: depths of views\n
    """

    class Chunk(object):

        def __init__(self, id, dataset, *,
                     indices: torch.Tensor, centers: torch.Tensor):
            """
            [summary]

            :param dataset `PanoDataset`: dataset object
            :param indices `Tensor(N)`: indices of views
            :param centers `Tensor(N, 3)`: centers of views
            """
            self.id = id
            self.dataset = dataset
            self.indices = indices
            self.centers = centers
            self.n_views = self.indices.size(0)
            self.n_pixels_per_view = self.dataset.res[0] * self.dataset.res[1]
            self.colors_cpu = None
            self.colors = None
            self.loaded = False

        def release(self):
            self.colors = None
            self.loaded = False
            MemProfiler.print_memory_stats(f'Chunk #{self.id} released')

        def load(self):
            if self.dataset.image_path is not None and self.colors_cpu is None:
                images = color.cvt(
                    img.load(self.dataset.image_path % i for i in self.indices),
                    color.RGB, self.dataset.c)
                if self.dataset.res != list(images.shape[-2:]):
                    images = nn_f.interpolate(images, self.dataset.res)
                self.colors_cpu = images.permute(0, 2, 3, 1).flatten(0, 2)
            if self.colors_cpu is not None:
                self.colors = self.colors_cpu.to(self.dataset.device)
            self.loaded = True
            MemProfiler.print_memory_stats(
                f'Chunk #{self.id} ({self.n_views} views, '
                f'{self.colors.numel() * self.colors.element_size() / 1024 / 1024:.2f}MB) loaded')

        def __len__(self):
            return self.n_views * self.n_pixels_per_view

        def __getitem__(self, idx):
            if not self.loaded:
                self.load()
            view_idx = idx // self.n_pixels_per_view
            pix_idx = idx % self.n_pixels_per_view
            extra_data = {}
            if self.colors is not None:
                extra_data['colors'] = self.colors[idx]
            rays_o = self.centers[view_idx]
            rays_d = self.dataset.pano_rays[pix_idx]
            return idx, rays_o, rays_d, extra_data

    def __init__(self, desc: dict, *,
                 c: int = color.RGB,
                 load_images: bool = True,
                 res: Tuple[int, int] = None,
                 views_to_load: Union[range, torch.Tensor] = None,
                 device: torch.device = None,
                 **kwargs):
        """
        Initialize data loader for spherical view synthesis task

        The dataset description file is a JSON file with following fields:

        - view_file_pattern: string, the path pattern of view images
        - view_res: { "x", "y" }, the resolution of view
        - depth_range: { "min", "max" }, the depth range
        - range: { "min": [...], "max": [...] }, the range of translation and rotation
        - centers: [ [ x, y, z ], ... ], centers of views

        :param desc_path ```str```: path to the data description file
        :param load_images ```bool```: whether load view images and return in __getitem__()
        :param c ```int```: color space to convert view images to
        :param calculate_rays ```bool```: whether calculate rays
        """
        self.c = c
        self.device = device
        self._load_desc(desc, data_dir, res, views_to_load, load_images)

    def get_data(self):
        return {
            'indices': self.indices,
            'centers': self.centers
        }

    def _load_desc(self, desc: dict,
                   res: Tuple[int, int],
                   views_to_load: Union[range, torch.Tensor],
                   load_images: bool):
        if load_images and desc.get('view_file_pattern'):
            self.image_path = os.path.join(os.getcwd(), desc['view_file_pattern'])
        else:
            self.image_path = None
        self.res = res if res else misc.values(desc['view_res'], 'y', 'x')
        self.depth_range = misc.values(desc['depth_range'], 'min', 'max') \
            if 'depth_range' in desc else None
        self.range = misc.values(desc['range'], 'min', 'max') if 'range' in desc else None
        self.samples = desc.get('samples')
        self.centers = torch.tensor(desc['view_centers'], device=self.device)  # (N, 3)
        self.indices = torch.tensor(
            desc['views'] if 'views' in desc else list(range(self.centers.size(0))),
            device=self.device)

        if views_to_load is not None:
            self.centers = self.centers[views_to_load]
            self.indices = self.indices[views_to_load]

        self.n_views = self.centers.size(0)
        self.n_pixels = self.n_views * self.res[0] * self.res[1]
        self.pano_rays = self._get_pano_rays()  # [H*W, 3]

        if desc.get('gl_coord'):
            print('Convert from OGL coordinate to DX coordinate (i. e. flip z axis)')
            self.centers[:, 2] *= -1

    def _get_pano_rays(self):
        """
        Get unprojected rays of pixels on a panorama

        :return `Tensor(H*W, 3)`: rays' directions with one unit length
        """
        spher_coords = torch.cat([
            torch.ones(*self.res, 1),
            ((misc.meshgrid(*self.res, normalize=True)) *
             torch.tensor([-2.0, 1.0]) + torch.tensor([1.5, 0.0])) * PI
        ], dim=-1).to(device=self.device)
        coords = sphere.spherical2cartesian(spher_coords)
        return coords.flatten(0, 1)  # [H*W, 3]