"""
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng

Hacked together by / Copyright 2020 Ross Wightman

Modified by Hangbo Bao, for generating the masked position for visual image transformer
"""
# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# Based on timm, DINO and DeiT code bases
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
# Copyright Zhun Zhong & Liang Zheng
#
# Hacked together by / Copyright 2020 Ross Wightman
#
# Modified by Hangbo Bao, for generating the masked position for visual image transformer
# --------------------------------------------------------'
import random
import math
import numpy as np


class MaskingGenerator:
    def __init__(
            self, input_size, num_masking_patches, min_num_patches=4, max_num_patches=None,
            min_aspect=0.3, max_aspect=None):
        if not isinstance(input_size, tuple):
            input_size = (input_size, ) * 2
        self.height, self.width = input_size

        self.num_patches = self.height * self.width
        self.num_masking_patches = num_masking_patches

        self.min_num_patches = min_num_patches
        self.max_num_patches = num_masking_patches if max_num_patches is None else max_num_patches

        max_aspect = max_aspect or 1 / min_aspect
        self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))

    def __repr__(self):
        repr_str = "Generator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % (
            self.height, self.width, self.min_num_patches, self.max_num_patches,
            self.num_masking_patches, self.log_aspect_ratio[0], self.log_aspect_ratio[1])
        return repr_str

    def get_shape(self):
        return self.height, self.width

    def _mask(self, mask, max_mask_patches):
        delta = 0
        for attempt in range(10):
            target_area = random.uniform(self.min_num_patches, max_mask_patches)
            aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
            h = int(round(math.sqrt(target_area * aspect_ratio)))
            w = int(round(math.sqrt(target_area / aspect_ratio)))
            if w < self.width and h < self.height:
                top = random.randint(0, self.height - h)
                left = random.randint(0, self.width - w)

                num_masked = mask[top: top + h, left: left + w].sum()
                # Overlap
                if 0 < h * w - num_masked <= max_mask_patches:
                    for i in range(top, top + h):
                        for j in range(left, left + w):
                            if mask[i, j] == 0:
                                mask[i, j] = 1
                                delta += 1

                if delta > 0:
                    break
        return delta

    def __call__(self):
        mask = np.zeros(shape=self.get_shape(), dtype=np.int32)
        mask_count = 0
        while mask_count < self.num_masking_patches:
            max_mask_patches = self.num_masking_patches - mask_count
            max_mask_patches = min(max_mask_patches, self.max_num_patches)

            delta = self._mask(mask, max_mask_patches)
            if delta == 0:
                break
            else:
                mask_count += delta
        
        # maintain a fix number {self.num_masking_patches}
        if mask_count > self.num_masking_patches:
            delta = mask_count - self.num_masking_patches
            mask_x, mask_y = mask.nonzero()
            to_vis = np.random.choice(mask_x.shape[0], delta, replace=False)
            mask[mask_x[to_vis], mask_y[to_vis]] = 0

        elif mask_count < self.num_masking_patches:
            delta = self.num_masking_patches - mask_count
            mask_x, mask_y = (mask == 0).nonzero()
            to_mask = np.random.choice(mask_x.shape[0], delta, replace=False)
            mask[mask_x[to_mask], mask_y[to_mask]] = 1

        assert mask.sum() == self.num_masking_patches, f"mask: {mask}, mask count {mask.sum()}"

        return mask


if __name__ == '__main__':
    import pdb
    generator = MaskingGenerator(input_size=14, num_masking_patches=118, min_num_patches=16,)
    for i in range(10000000):
        mask = generator()
        if mask.sum() != 118:
            pdb.set_trace()
            print(mask)
            print(mask.sum())