import math
import random

from PIL import Image
import blobfile as bf
#from mpi4py import MPI
import numpy as np
from torch.utils.data import DataLoader, Dataset
import os
import torchvision.transforms as transforms
import torch as th
from .degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
from functools import partial
import cv2



def get_params( size,  resize_size,  crop_size):
    w, h = size
    new_h = h
    new_w = w

    ss, ls = min(w, h), max(w, h)  # shortside and longside
    width_is_shorter = w == ss
    ls = int(resize_size * ls / ss)
    ss = resize_size
    new_w, new_h = (ss, ls) if width_is_shorter else (ls, ss)

    x = random.randint(0, np.maximum(0, new_w - crop_size))
    y = random.randint(0, np.maximum(0, new_h - crop_size))

    flip = random.random() > 0.5
    return {'crop_pos': (x, y), 'flip': flip}
 

def get_transform(params,  resize_size,  crop_size, method=Image.BICUBIC,  flip=True, crop = True):
    transform_list = []
 
    transform_list.append(transforms.Lambda(lambda img: __scale(img, crop_size, method)))

    if flip:
        transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip'])))
 
    return transforms.Compose(transform_list)

def get_tensor(normalize=True, toTensor=True):
    transform_list = []
    if toTensor:
        transform_list += [transforms.ToTensor()]

    if normalize:
        transform_list += [transforms.Normalize((0.5, 0.5, 0.5),
                                                (0.5, 0.5, 0.5))]
    return transforms.Compose(transform_list)

def normalize():
    return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))


def __scale(img, target_width, method=Image.BICUBIC):
    return img.resize((target_width, target_width), method)

def __flip(img, flip):
    if flip:
        return img.transpose(Image.FLIP_LEFT_RIGHT)
    return img