from_fmcib.py

""" from `fmcib/ssl/transforms/duplicate.py` """
from typing import Any, Callable, List, Optional, Tuple

from copy import deepcopy

import torch


class Duplicate:
    """Duplicate an input and apply two different transforms. Used for SimCLR primarily."""

    def __init__(self, transforms1: Optional[Callable] = None, transforms2: Optional[Callable] = None):
        """Duplicates an input and applies the given transformations to each copy separately.

        Args:
            transforms1 (Optional[Callable], optional): _description_. Defaults to None.
            transforms2 (Optional[Callable], optional): _description_. Defaults to None.
        """
        # Wrapped into a list if it isn't one already to allow both a
        # list of transforms as well as `torchvision.transform.Compose` transforms.
        self.transforms1 = transforms1
        self.transforms2 = transforms2

    def __call__(self, input: Any) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Args:
            input (torch.Tensor or any other type supported by the given transforms): Input.

        Returns:
            Tuple[torch.Tensor, torch.Tensor]: a tuple of two tensors.
        """
        out1, out2 = input, deepcopy(input)
        if self.transforms1 is not None:
            out1 = self.transforms1(out1)
        if self.transforms2 is not None:
            out2 = self.transforms2(out2)
        return (out1, out2)








""" from `fmcib/ssl/transforms/random_resized_crop.py` """
from typing import Any, Dict, List

import torch
from monai.transforms import RandScaleCrop, Resize, Transform


class RandomResizedCrop3D(Transform):
    """
    Combines monai's random spatial crop followed by resize to the desired size.

    Modification:
    1. The spatial crop is done with same dimensions for all the axes
    2. Handles cases where the image_size is less than the crop_size by choosing
        the smallest dimension as the random scale.

    """

    def __init__(self, prob: float = 1, size: int = 50, scale: List[float] = [0.5, 1.0]):
        """
        Args:
            scale (List[int]): Specifies the lower and upper bounds for the random area of the crop,
             before resizing. The scale is defined with respect to the area of the original image.
        """
        super().__init__()
        self.prob = prob
        self.scale = scale
        self.size = [size] * 3

    def __call__(self, image):
        if torch.rand(1) < self.prob:
            random_scale = torch.empty(1).uniform_(*self.scale).item()
            rand_cropper = RandScaleCrop(random_scale, random_size=False)
            resizer = Resize(self.size, mode="trilinear")

            for transform in [rand_cropper, resizer]:
                image = transform(image)

        return image