Move sampler logic to samplers module and add unit tests

src/eva/vision/data/wsi/patching/samplers/__init__.py

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+"""Patch Sampler API."""
+
+from eva.vision.data.wsi.patching.samplers.base import ForegroundSampler, Sampler
+from eva.vision.data.wsi.patching.samplers.foreground_grid import ForegroundGridSampler
+from eva.vision.data.wsi.patching.samplers.grid import GridSampler
+from eva.vision.data.wsi.patching.samplers.random import RandomSampler
+
+__all__ = ["Sampler", "ForegroundSampler", "RandomSampler", "GridSampler", "ForegroundGridSampler"]

src/eva/vision/data/wsi/patching/samplers/_utils.py

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+import random
+from typing import Tuple
+
+import numpy as np
+
+
+def set_seed(seed: int) -> None:
+    random.seed(seed)
+    np.random.seed(seed)
+
+
+def get_grid_coords_and_indices(
+    layer_shape: Tuple[int, int],
+    width: int,
+    height: int,
+    overlap: Tuple[int, int],
+    shuffle: bool = True,
+    seed: int = 42,
+):
+    """Get grid coordinates and indices.
+
+    Args:
+        layer_shape: The shape of the layer.
+        width: The width of the patches.
+        height: The height of the patches.
+        overlap: The overlap between patches in the grid.
+        shuffle: Whether to shuffle the indices.
+        seed: The random seed.
+    """
+    x_range = range(0, layer_shape[0] - width + 1, width - overlap[0])
+    y_range = range(0, layer_shape[1] - height + 1, height - overlap[1])
+    x_y = [(x, y) for x in x_range for y in y_range]
+
+    indices = list(range(len(x_y)))
+    if shuffle:
+        set_seed(seed)
+        np.random.shuffle(indices)
+    return x_y, indices
+
+
+def validate_dimensions(width: int, height: int, layer_shape: Tuple[int, int]) -> None:
+    """Checks if the width / height is bigger than the layer shape.
+
+    Args:
+        width: The width of the patches.
+        height: The height of the patches.
+        layer_shape: The shape of the layer.
+    """
+    if width > layer_shape[0] or height > layer_shape[1]:
+        raise ValueError("The width / height cannot be bigger than the layer shape.")

src/eva/vision/data/wsi/patching/samplers/base.py

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+"""Base classes for samplers."""
+
+import abc
+from typing import Generator, Tuple
+
+from eva.vision.data.wsi.patching.mask import Mask
+
+
+class Sampler(abc.ABC):
+    """Base class for samplers."""
+
+    @abc.abstractmethod
+    def sample(
+        self,
+        width: int,
+        height: int,
+        layer_shape: Tuple[int, int],
+        mask: Mask | None = None,
+    ) -> Generator[Tuple[int, int], None, None]:
+        """Sample patche coordinates.
+
+        Args:
+            width: The width of the patches.
+            height: The height of the patches.
+            layer_shape: The shape of the layer.
+            mask: Tuple containing the mask array and the scaling factor with respect to the
+                provided layer_shape. Optional, only required for samplers with foreground
+                filtering.
+
+        Returns:
+            A generator producing sampled patch coordinates.
+        """
+
+
+class ForegroundSampler(Sampler):
+    """Base class for samplers with foreground filtering capabilities."""
+
+    @abc.abstractmethod
+    def is_foreground(
+        self,
+        mask: Mask,
+        x: int,
+        y: int,
+        width: int,
+        height: int,
+        min_foreground_ratio: float,
+    ) -> bool:
+        """Check if a patch contains sufficient foreground."""