import plotly_express as px
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as Vision
import cv2
import math
import numpy as np
import os


def DumpTensorToImage(tensor, name, autoPad=True, scale=1.0, auto2d=True):
    if len(tensor.shape) != 2 and len(tensor.shape) != 1:
        raise ("Error input dims")

    tensor = tensor.float()
    maxv = torch.max(tensor)
    minv = torch.min(tensor)
    tensor = (((tensor - minv) / (maxv - minv)) * 255).byte().cpu()
    img = tensor.numpy()
    srp = img.shape

    if auto2d and len(srp) == 1:
        ceiled = math.ceil((srp[0]) ** 0.5)
        img = cv2.copyMakeBorder(img, 0, ceiled * ceiled - srp[0], 0, 0, 0)
        img = img.reshape((ceiled, ceiled))
        srp = img.shape
    if autoPad and (max(srp) / min(srp) > 16):
        img = cv2.resize(img, [max(srp), max(srp)])
        srp = img.shape
    if scale != 1.0:
        img = cv2.resize(img, [int(srp[0] * scale), int(srp[1] * scale)])
        srp = img.shape
    cv2.imwrite(name, img)


def DumpTensorToLog(tensor, name="log"):
    shape = tensor.shape
    f = open(name, "w")
    data = tensor.reshape([-1]).float().cpu().numpy().tolist()
    for d in data:
        f.writelines("%s" % d + os.linesep)
    f.close()


def DumpTensorToFile(tensor, name="tensor.pt"):
    torch.save(tensor.cpu(), name)


def LoadTensorToFile(name="tensor.pt"):
    return torch.load(name)


def DumpListToFile(list, name="list"):
    f = open(name, "w")
    for d in list:
        f.writelines("%s" % d + os.linesep)
    f.close()