from __future__ import absolute_import

import random
import math
import torch


def _get_patch(per_pixel, rand_color, patch_size, dtype=torch.float32, device='cuda'):
    if per_pixel:
        return torch.empty(
            patch_size, dtype=dtype, device=device).normal_()
    elif rand_color:
        return torch.empty((patch_size[0], 1, 1), dtype=dtype, device=device).normal_()
    else:
        return torch.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)


class RandomErasing:
    """ Randomly selects a rectangle region in an image and erases its pixels.
        'Random Erasing Data Augmentation' by Zhong et al.
        See https://arxiv.org/pdf/1708.04896.pdf

        This variant of RandomErasing is intended to be applied to either a batch
        or single image tensor after it has been normalized by dataset mean and std.
    Args:
         probability: The probability that the Random Erasing operation will be performed.
         sl: Minimum proportion of erased area against input image.
         sh: Maximum proportion of erased area against input image.
         min_aspect: Minimum aspect ratio of erased area.
         per_pixel: random value for each pixel in the erase region, precedence over rand_color
         rand_color: random color for whole erase region, 0 if neither this or per_pixel set
    """

    def __init__(
            self,
            probability=0.5, sl=0.02, sh=1/3, min_aspect=0.3,
            per_pixel=False, rand_color=False, device='cuda'):
        self.probability = probability
        self.sl = sl
        self.sh = sh
        self.min_aspect = min_aspect
        self.per_pixel = per_pixel  # per pixel random, bounded by [pl, ph]
        self.rand_color = rand_color  # per block random, bounded by [pl, ph]
        self.device = device

    def _erase(self, img, chan, img_h, img_w, dtype):
        if random.random() > self.probability:
            return
        area = img_h * img_w
        for attempt in range(100):
            target_area = random.uniform(self.sl, self.sh) * area
            aspect_ratio = random.uniform(self.min_aspect, 1 / self.min_aspect)
            h = int(round(math.sqrt(target_area * aspect_ratio)))
            w = int(round(math.sqrt(target_area / aspect_ratio)))
            if w < img_w and h < img_h:
                top = random.randint(0, img_h - h)
                left = random.randint(0, img_w - w)
                img[:, top:top + h, left:left + w] = _get_patch(
                    self.per_pixel, self.rand_color, (chan, h, w), dtype=dtype, device=self.device)
                break

    def __call__(self, input):
        if len(input.size()) == 3:
            self._erase(input, *input.size(), input.dtype)
        else:
            batch_size, chan, img_h, img_w = input.size()
            for i in range(batch_size):
                self._erase(input[i], chan, img_h, img_w, input.dtype)
        return input