Merge branch 'add-mnist-datasets' of https://github.com/Safe-DS/Datasets

 into add-mnist-datasets

# Conflicts:
#	src/safeds_datasets/image/_mnist/_mnist.py

src/safeds_datasets/image/_mnist/_mnist.py

@@ -29,12 +29,34 @@
 
 _fashion_mnist_links: list[str] = ["http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/"]
 _fashion_mnist_files: dict[str, str] = _mnist_files
-_fashion_mnist_labels: dict[int, str] = {0: "T-shirt/top", 1: "Trouser", 2: "Pullover", 3: "Dress", 4: "Coat", 5: "Sandal", 6: "Shirt", 7: "Sneaker", 8: "Bag", 9: "Ankle boot"}
+_fashion_mnist_labels: dict[int, str] = {
+    0: "T-shirt/top",
+    1: "Trouser",
+    2: "Pullover",
+    3: "Dress",
+    4: "Coat",
+    5: "Sandal",
+    6: "Shirt",
+    7: "Sneaker",
+    8: "Bag",
+    9: "Ankle boot",
+}
 _fashion_mnist_folder: str = "fashion-mnist"
 
 _kuzushiji_mnist_links: list[str] = ["http://codh.rois.ac.jp/kmnist/dataset/kmnist/"]
 _kuzushiji_mnist_files: dict[str, str] = _mnist_files
-_kuzushiji_mnist_labels: dict[int, str] = {0: "\u304a", 1: "\u304d", 2: "\u3059", 3: "\u3064", 4: "\u306a", 5: "\u306f", 6: "\u307e", 7: "\u3084", 8: "\u308c", 9: "\u3092"}
+_kuzushiji_mnist_labels: dict[int, str] = {
+    0: "\u304a",
+    1: "\u304d",
+    2: "\u3059",
+    3: "\u3064",
+    4: "\u306a",
+    5: "\u306f",
+    6: "\u307e",
+    7: "\u3084",
+    8: "\u308c",
+    9: "\u3092",
+}
 _kuzushiji_mnist_folder: str = "kmnist"
 
 
@@ -68,7 +90,11 @@ def load_mnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[Co
             missing_files.append(file_path)
     if len(missing_files) > 0:
         if download:
-            _download_mnist_like(path, {name: f_path for name, f_path in _mnist_files.items() if f_path in missing_files}, _mnist_links)
+            _download_mnist_like(
+                path,
+                {name: f_path for name, f_path in _mnist_files.items() if f_path in missing_files},
+                _mnist_links,
+            )
         else:
             raise FileNotFoundError(f"Could not find files {[str(path / file) for file in missing_files]}")
     return _load_mnist_like(path, _mnist_files, _mnist_labels)
@@ -104,7 +130,11 @@ def load_fashion_mnist(path: str | Path, download: bool = True) -> tuple[ImageDa
             missing_files.append(file_path)
     if len(missing_files) > 0:
         if download:
-            _download_mnist_like(path, {name: f_path for name, f_path in _fashion_mnist_files.items() if f_path in missing_files}, _fashion_mnist_links)
+            _download_mnist_like(
+                path,
+                {name: f_path for name, f_path in _fashion_mnist_files.items() if f_path in missing_files},
+                _fashion_mnist_links,
+            )
         else:
             raise FileNotFoundError(f"Could not find files {[str(path / file) for file in missing_files]}")
     return _load_mnist_like(path, _fashion_mnist_files, _fashion_mnist_labels)
@@ -140,13 +170,21 @@ def load_kmnist(path: str | Path, download: bool = True) -> tuple[ImageDataset[C
             missing_files.append(file_path)
     if len(missing_files) > 0:
         if download:
-            _download_mnist_like(path, {name: f_path for name, f_path in _kuzushiji_mnist_files.items() if f_path in missing_files}, _kuzushiji_mnist_links)
+            _download_mnist_like(
+                path,
+                {name: f_path for name, f_path in _kuzushiji_mnist_files.items() if f_path in missing_files},
+                _kuzushiji_mnist_links,
+            )
         else:
             raise FileNotFoundError(f"Could not find files {[str(path / file) for file in missing_files]}")
     return _load_mnist_like(path, _kuzushiji_mnist_files, _kuzushiji_mnist_labels)
 
 
-def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int, str]) -> tuple[ImageDataset[Column], ImageDataset[Column]]:
+def _load_mnist_like(
+    path: str | Path,
+    files: dict[str, str],
+    labels: dict[int, str],
+) -> tuple[ImageDataset[Column], ImageDataset[Column]]:
     _init_default_device()
 
     path = Path(path)
@@ -156,23 +194,29 @@ def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int,
     train_image_list: ImageList | None = None
     for file_name, file_path in files.items():
         if "idx1" in file_name:
-            with gzip.open(path / file_path, mode='rb') as label_file:
+            with gzip.open(path / file_path, mode="rb") as label_file:
                 magic, size = struct.unpack(">II", label_file.read(8))
                 if magic != 2049:
                     raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2049.")  # pragma: no cover
                 if "train" in file_name:
-                    train_labels = Column(file_name, [labels[label_index] for label_index in array("B", label_file.read())])
+                    train_labels = Column(
+                        file_name,
+                        [labels[label_index] for label_index in array("B", label_file.read())],
+                    )
                 else:
                     test_labels = Column(file_name, [labels[label_index] for label_index in array("B", label_file.read())])
         else:
-            with gzip.open(path / file_path, mode='rb') as image_file:
+            with gzip.open(path / file_path, mode="rb") as image_file:
                 magic, size, rows, cols = struct.unpack(">IIII", image_file.read(16))
                 if magic != 2051:
                     raise ValueError(f"Magic number mismatch. Actual {magic} != Expected 2051.")  # pragma: no cover
                 image_data = array("B", image_file.read())
                 image_tensor = torch.empty(size, 1, rows, cols)
                 for i in range(size):
-                    image_tensor[i, 0] = torch.frombuffer(image_data[i * rows * cols:(i + 1) * rows * cols], dtype=torch.uint8).reshape(rows, cols)
+                    image_tensor[i, 0] = torch.frombuffer(
+                        image_data[i * rows * cols : (i + 1) * rows * cols],
+                        dtype=torch.uint8,
+                    ).reshape(rows, cols)
                 image_list = _SingleSizeImageList()
                 image_list._tensor = image_tensor
                 image_list._tensor_positions_to_indices = list(range(size))
@@ -183,7 +227,11 @@ def _load_mnist_like(path: str | Path, files: dict[str, str], labels: dict[int,
                     test_image_list = image_list
     if train_image_list is None or test_image_list is None or train_labels is None or test_labels is None:
         raise ValueError  # pragma: no cover
-    return ImageDataset[Column](train_image_list, train_labels, 32, shuffle=True), ImageDataset[Column](test_image_list, test_labels, 32)
+    return ImageDataset[Column](train_image_list, train_labels, 32, shuffle=True), ImageDataset[Column](
+        test_image_list,
+        test_labels,
+        32,
+    )
 
 
 def _download_mnist_like(path: str | Path, files: dict[str, str], links: list[str]) -> None: