# Glue layer for transforming whole pictures into 384x384 sequence for encoder input
from dataclasses import dataclass
from itertools import product # product library

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import transforms

import numpy as np
from torchvision.transforms import v2

# The v2 way, apparantly. [1]
class SquareCropTransformLayer(nn.Module):
    def __init__(self, crop_size: int):
        super(SquareCropTransformLayer, self).__init__()
        self.crop_size = crop_size

    def forward(
            self,
            x_: torch.Tensor,
            kpoints_: torch.Tensor
    ) -> (torch.Tensor, torch.Tensor):
        # Here, x_ & kpoints_ already applied affine transform.
        assert len(x_.shape) == 4
        batch_size, channels, height, width = x_.shape
        h_split_count = height // self.crop_size
        w_split_count = width // self.crop_size

        # Perform identical splits -- note kpoints_ does not have C dimension!
        ret_x = x_.view(
            batch_size * h_split_count * w_split_count,
            channels,
            self.crop_size,
            self.crop_size,
        )
        split_t = kpoints_.view(
            batch_size * h_split_count * w_split_count,
            self.crop_size,
            self.crop_size,
        )

        # Sum into gt_count
        ret_gt_count = (torch
            .sum(split_t.view(split_t.size(0), -1), dim=1)
            .unsqueeze(1)
        )

        return ret_x, ret_gt_count

"""
References:
[1] https://pytorch.org/vision/stable/auto_examples/transforms/plot_custom_transforms.html
"""