#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use 
# under the terms of the LICENSE.md file.
#
# For inquiries contact  george.drettakis@inria.fr
#

import torch
import sys
from datetime import datetime
import numpy as np
import random
import os
import cv2

def inverse_sigmoid(x):
    return torch.log(x/(1-x))

def PILtoTorch(pil_image, resolution):
    resized_image_PIL = pil_image.resize(resolution)
    resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0
    if len(resized_image.shape) == 3:
        return resized_image.permute(2, 0, 1)
    else:
        return resized_image.unsqueeze(dim=-1).permute(2, 0, 1)
    
def PIL2toTorch(pil_image, resolution):
    resized_image_PIL = pil_image.resize(resolution) 
    resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0 * (2.0 ** 16 - 1.0)
    return resized_image

def decode_op(optical_png):
    # use 'PIL Image.Open' to READ 
    "Convert from .png (h, w, 3-rgb) -> (h,w,2)(flow_x, flow_y) .. float32 array"
    optical_png = optical_png[..., [2, 1, 0]]   # bgr -> rgb
    h, w, _c = optical_png.shape
    assert optical_png.dtype == np.uint16 and _c == 3
    "invalid flow flag: b == 0 for sky or other invalid flow"
    invalid_points = np.where(optical_png[..., 2] == 0)
    out_flow = torch.empty((h, w, 2))
    decoded = 2.0 / (2**16 - 1.0) * optical_png.astype('f4') - 1
    out_flow[..., 0] = torch.tensor(decoded[:, :, 0] * (w - 1))  # (pixel) delta_x : R
    out_flow[..., 1] = torch.tensor(decoded[:, :, 1] * (h - 1)) # delta_y : G
    out_flow[invalid_points[0], invalid_points[1], :] = 0  # B=0 for invalid flow
    return out_flow

def get_expon_lr_func(
    lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000
):
    """
    Copied from Plenoxels

    Continuous learning rate decay function. Adapted from JaxNeRF
    The returned rate is lr_init when step=0 and lr_final when step=max_steps, and
    is log-linearly interpolated elsewhere (equivalent to exponential decay).
    If lr_delay_steps>0 then the learning rate will be scaled by some smooth
    function of lr_delay_mult, such that the initial learning rate is
    lr_init*lr_delay_mult at the beginning of optimization but will be eased back
    to the normal learning rate when steps>lr_delay_steps.
    :param conf: config subtree 'lr' or similar
    :param max_steps: int, the number of steps during optimization.
    :return HoF which takes step as input
    """

    def helper(step):
        if step < 0 or (lr_init == 0.0 and lr_final == 0.0):
            # Disable this parameter
            return 0.0
        if lr_delay_steps > 0:
            # A kind of reverse cosine decay.
            delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin(
                0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1)
            )
        else:
            delay_rate = 1.0
        t = np.clip(step / max_steps, 0, 1)
        log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t)
        return delay_rate * log_lerp

    return helper

def strip_lowerdiag(L):
    uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda")

    uncertainty[:, 0] = L[:, 0, 0]
    uncertainty[:, 1] = L[:, 0, 1]
    uncertainty[:, 2] = L[:, 0, 2]
    uncertainty[:, 3] = L[:, 1, 1]
    uncertainty[:, 4] = L[:, 1, 2]
    uncertainty[:, 5] = L[:, 2, 2]
    return uncertainty

def strip_symmetric(sym):
    return strip_lowerdiag(sym)

def build_rotation(r):
    norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3])

    q = r / norm[:, None]

    R = torch.zeros((q.size(0), 3, 3), device='cuda')

    r = q[:, 0]
    x = q[:, 1]
    y = q[:, 2]
    z = q[:, 3]

    R[:, 0, 0] = 1 - 2 * (y*y + z*z)
    R[:, 0, 1] = 2 * (x*y - r*z)
    R[:, 0, 2] = 2 * (x*z + r*y)
    R[:, 1, 0] = 2 * (x*y + r*z)
    R[:, 1, 1] = 1 - 2 * (x*x + z*z)
    R[:, 1, 2] = 2 * (y*z - r*x)
    R[:, 2, 0] = 2 * (x*z - r*y)
    R[:, 2, 1] = 2 * (y*z + r*x)
    R[:, 2, 2] = 1 - 2 * (x*x + y*y)
    return R

def build_scaling_rotation(s, r):
    L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda")
    R = build_rotation(r)

    L[:,0,0] = s[:,0]
    L[:,1,1] = s[:,1]
    L[:,2,2] = s[:,2]

    L = R @ L
    return L

DEFAULT_RANDOM_SEED = 0

def seedBasic(seed=DEFAULT_RANDOM_SEED):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)

def seedTorch(seed=DEFAULT_RANDOM_SEED):
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
      
# basic + tensorflow + torch 
def seedEverything(seed=DEFAULT_RANDOM_SEED):
    seedBasic(seed)
    seedTorch(seed)

def safe_state(silent):
    old_f = sys.stdout
    class F:
        def __init__(self, silent):
            self.silent = silent

        def write(self, x):
            if not self.silent:
                if x.endswith("\n"):
                    old_f.write(x.replace("\n", " [{}]\n".format(str(datetime.now().strftime("%d/%m %H:%M:%S")))))
                else:
                    old_f.write(x)

        def flush(self):
            old_f.flush()

    sys.stdout = F(silent)

    random.seed(DEFAULT_RANDOM_SEED)
    np.random.seed(DEFAULT_RANDOM_SEED)
    torch.manual_seed(DEFAULT_RANDOM_SEED)
    torch.cuda.set_device(torch.device("cuda:0"))
    # sys.stdout = old_f