From 40f67901b5f534f2b044988076571800f49c7c6c Mon Sep 17 00:00:00 2001
From: Leif Van Holland <holland@cs.uni-bonn.de>
Date: Wed, 28 Jan 2026 10:20:38 +0000
Subject: [PATCH] updated readme and fixed typing tests

---
 .gitignore                                    |   3 +-
 .pre-commit-config.yaml                       |   1 +
 README.md                                     | 107 +++++++------
 .../benchmark_overhead_additional_args_0.png  | Bin 0 -> 24302 bytes
 .../benchmark_overhead_additional_args_10.png | Bin 0 -> 24933 bytes
 ...benchmark_overhead_additional_args_100.png | Bin 0 -> 25203 bytes
 noxfile.py                                    |   1 -
 setup.py                                      |   2 +-
 speedtest.py                                  | 137 ++++++++++------
 speedtest_plot.ipynb                          | 149 ++++++++++++++++++
 src/tensor_shape_assert/__init__.py           |   2 +-
 src/tensor_shape_assert/types.py              |  13 +-
 src/tensor_shape_assert_test.py               |  22 +--
 src/tensor_shape_assert_typesafe_test.py      |  78 ++++-----
 src/test_utils.py                             |  19 +++
 15 files changed, 369 insertions(+), 165 deletions(-)
 create mode 100644 .pre-commit-config.yaml
 create mode 100644 assets/benchmark_overhead_additional_args_0.png
 create mode 100644 assets/benchmark_overhead_additional_args_10.png
 create mode 100644 assets/benchmark_overhead_additional_args_100.png
 create mode 100644 speedtest_plot.ipynb
 create mode 100644 src/test_utils.py

diff --git a/.gitignore b/.gitignore
index 5e5bbf0..e89da07 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,4 +4,5 @@ build
 **/.nox
 **/.pytest_cache
 .vscode
-**/.mypy_cache
\ No newline at end of file
+**/.mypy_cache
+benchmark_results.csv
\ No newline at end of file
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..a62c209
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1 @@
+repos: []
\ No newline at end of file
diff --git a/README.md b/README.md
index 1bc60ec..e063d44 100644
--- a/README.md
+++ b/README.md
@@ -7,9 +7,45 @@ A minimal utility library that
 * supports any array class that exposes a ``shape`` property,
 * and a lot more!
 
+## Installation
+
+Currently, the package can only be installed directly from the repository with
+```bash
+pip install git+https://github.com/leifvan/tensor-shape-assert
+```
+
 ## Usage
+
 Decorate functions with ``@check_tensor_shapes()`` and any parameter with a type hint of type ``ShapedTensor[<desc>]`` will be dynamically checked for the correct shape. A shape descriptor is a *string* of space-separated length descriptions for each dimension. The return value can also be annotated in the same way.
 
+### Simple example
+
+```python
+import torch
+from .tensor_shape_assert import check_tensor_shapes, ShapedTensor
+
+@check_tensor_shapes()
+def my_simple_func(
+        x: ShapedTensor["a b 3"],
+        y: ShapedTensor["b 2"]
+) -> ShapedTensor["a"]:
+
+    z = x[:, :, :2] + y[None]
+    return (z[:, :, 0] * z[:, :, 1]).sum(dim=1)
+```
+
+Calling it like this
+```python
+my_simple_func(torch.zeros(5, 4, 3), y=torch.zeros(4, 2)) # works
+```
+passes the test, because ``a=5 and b=4`` matches for both input and output annotations.
+
+For
+```python
+my_simple_func(torch.zeros(5, 4, 3), y=torch.zeros(4, 3)) # fails
+```
+the test fails, because `y` is expected to have length 2 in the second dimension.
+
 ### Integers
 
  * Sizes can be defined explicitly as an integer, e.g. ``"5 3"`` (only arrays of shape ``(5, 3)`` are valid)
@@ -44,51 +80,11 @@ There are convenience functions that access the current states of the shape vari
 
 You can even go one step further and do a check tensors inside the wrapped function directly with ``assert_shape_here(x, <desc>)``, which will run a check on the object or shape ``x`` given the descriptor and add previously unseen variables in the descriptor to the state inside the wrapped function. This way you can check the output of the function against tensor shapes that only appear in the body of the function.
 
-## Installation
-
-Currently, the package can only be installed directly from the repository with
-```bash
-pip install git+https://github.com/leifvan/tensor-shape-assert
-```
-
 ## Compatibility
 
 While the examples below are using PyTorch, *tensor-shape-assert* requires very minimal functionality and is compatible with any array class that has a ``shape`` method, which includes popular frameworks such as NumPy, TensorFlow, Jax and more generally frameworks that conform to the [Python array API standard](https://data-apis.org/array-api/latest/).
 
-## Examples
-
-Here are two examples that demonstrate how the annotation works.
-
-### Simple example
-
-```python
-import torch
-from .tensor_shape_assert import check_tensor_shapes, ShapedTensor
-
-@check_tensor_shapes()
-def my_simple_func(
-        x: ShapedTensor["a b 3"],
-        y: ShapedTensor["b 2"]
-) -> ShapedTensor["a"]:
-
-    z = x[:, :, :2] + y[None]
-    return (z[:, :, 0] * z[:, :, 1]).sum(dim=1)
-```
-
-Calling it like this
-```python
-my_simple_func(torch.zeros(5, 4, 3), y=torch.zeros(4, 2)) # works
-```
-passes the test, because ``a=5 and b=4`` matches for both input and output annotations.
-
-For
-```python
-my_simple_func(torch.zeros(5, 4, 3), y=torch.zeros(4, 3)) # fails
-```
-the test fails, because `y` is expected to have length 2 in the second dimension.
-
----
-### Complex example
+## More Examples
 
 The complex example additionally contains tuple and optional annotations.
 ```python
@@ -186,6 +182,25 @@ def my_func(x: ShapedTensor["n k"], k: int):
 my_func(torch.zeros(10, 2), k=2) # works
 my_func(torch.zeros(10, 2), k=3) # works
 ```
+---
+### Type safety (new in version 0.3)
+
+If you are using static type checkers like MyPy, you can use the more verbose but type safe literal syntax. For this, you are also required to specify the array type. You can either do this manually with ``ShapedLiteral``, or use the predefined aliases ``ShapedTorchLiteral``, ``ShapedNumpyLiteral``. The example will show both options for PyTorch.
+
+```python
+from typing import Literal as L
+
+@check_tensor_shapes()
+def my_simple_func(
+        x: ShapedTorchLiteral[L["a b 3"]],
+        y: ShapedTorchLiteral[L["b 2"]]
+) -> ShapedLiteral[torch.Tensor, L["a"]]:
+
+    z = x[:, :, :2] + y[None]
+    return (z[:, :, 0] * z[:, :, 1]).sum(dim=1)
+```
+
+Another benefit from using the typed version is that tooltips in VS Code are more helpful, as they can pass trough the ``Literal`` string. This way you can check the annotated shape without having to open the file with the annotated code.
 
 ## Known bugs
 * [ ] ``get_shape_variables`` does not work if checks are disabled. This should be possible but give a performance warning, recommending not to use this feature in performance-critical applications.
@@ -208,7 +223,7 @@ reraise
   * [x] donnx
   * [x] sparse
   * ~~[ ] cupy~~ (we leave this out for now because it requires CUDA)
-* [ ] check compatibility with static type checkers
+* [x] check compatibility with static type checkers
 * [ ] rewrite README to give a cleaner overview over the features
 * [ ] support union of shape descriptors (but this might break the current simplicity)
 * [ ] benchmark speed to understand impact in tight loops
@@ -216,11 +231,11 @@ reraise
 * ~~[ ] device annotation~~ (device definition not standardized in Python array API 2024.12, see [this section of the specifications](https://data-apis.org/array-api/2024.12/design_topics/device_support.html#device-support))
 * [ ] add variable names for dtype
 * [ ] add more helpful message when parameter / output is not the expected type
-* [ ] (maybe instead of the one before) catch and reraise all exceptions inside
+* [x] (maybe instead of the one before) catch and reraise all exceptions inside
  wrapper and reraise with additional info about exception location
-* [ ] improve hints for static type checking (currently it assumes either torch
+* [x] improve hints for static type checking (currently it assumes either torch
  or the object just having a .shape parameter)
-* [ ] come up with a way to allow union of shape descriptors
 * [ ] make get_shape_variables work in check modes "never" and "once" without
  performance overhead
-* [ ] work on performance overhead in general
\ No newline at end of file
+* [ ] work on performance overhead in general
+
diff --git a/assets/benchmark_overhead_additional_args_0.png b/assets/benchmark_overhead_additional_args_0.png
new file mode 100644
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diff --git a/assets/benchmark_overhead_additional_args_10.png b/assets/benchmark_overhead_additional_args_10.png
new file mode 100644
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HcmV?d00001

diff --git a/noxfile.py b/noxfile.py
index 6e87c4f..ed760c2 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -41,4 +41,3 @@ def typecheck(session):
     session.install("numpy", "torch")
     session.install("mypy")
     session.run("mypy", "src")
-
diff --git a/setup.py b/setup.py
index 956a480..b394d6e 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@
 
 setup(
     name="tensor-shape-assert",
-    version="0.2.6",
+    version="0.3.0",
     description="A simple runtime assert library for tensor-based frameworks.",
     long_description=long_description,
     long_description_content_type="text/markdown",
diff --git a/speedtest.py b/speedtest.py
index 329773d..b9588b4 100644
--- a/speedtest.py
+++ b/speedtest.py
@@ -1,61 +1,96 @@
-from src.tensor_shape_assert import ShapedTensor, check_tensor_shapes, set_global_check_mode
-import torch
+from src.tensor_shape_assert import ShapedTensor, check_tensor_shapes
+from test_utils import get_library_by_name, NAME_LIBRARY_MAP
 from time import time
-from typing import NamedTuple
+from tqdm import tqdm
+from tabulate import tabulate
 
 
-def benchmark(f, num_runs, num_additonal_args):
-    start_time = time()
-
-    add = [torch.zeros(10, 5) for _ in range(num_additonal_args)]
-
-    for _ in range(num_runs):
-        f(
-             torch.zeros((10, 20, 30)),
-             torch.zeros((20, 2)),
-             *add
-        )
-    return (time() - start_time) / num_runs
-
-def func(x: ShapedTensor["a b c"], y: ShapedTensor["b d"], *args: tuple[ShapedTensor["10 5"], ...]) -> ShapedTensor["a d"]:
-        k = sum(args)
-        z = x[..., None] * y[None, :, None]  # a b c d
-        return z.sum(dim=(1, 2))  # a d
-
-if __name__ == "__main__":
-
-    num_runs = 10000
-    for num_add_args in (0, 10, 100):
+def benchmark(f, num_additonal_args, xp):
 
-        func_always = check_tensor_shapes(check_mode="always")(func)
-        func_once = check_tensor_shapes(check_mode="once")(func)
-        func_never = check_tensor_shapes(check_mode="never")(func)
+    # prepare args
+    x = xp.zeros((10, 20, 30))
+    y = xp.zeros((20, 2))
+    add = [xp.zeros((10, 5)) for _ in range(num_additonal_args)]
 
-        # full shape checking
+    # some warmup
+    for _ in range(2 ** 7):
+        f(x, y, *add)
 
-        duration_with = benchmark(func_always, num_runs=num_runs, num_additonal_args=num_add_args)
-
-        # check once
-        
-        set_global_check_mode("once")
-
-        duration_global_check_once = benchmark(func_once, num_runs=num_runs, num_additonal_args=num_add_args)
-
-        # disabled global checking
-        
-        set_global_check_mode("never")
-
-        duration_global_check_never = benchmark(func_never, num_runs=num_runs, num_additonal_args=num_add_args)
+    # actual benchmark
+    start_time = time()
+    for i in tqdm(range(2 ** 16)):
+        f(x, y, *add)
 
-        # no annotations
+        if i % 500 == 0:
+            if time() - start_time > 5:
+                break
+    return (time() - start_time) / (i + 1)  # type: ignore
 
-        duration_not_annotated = benchmark(func, num_runs=num_runs, num_additonal_args=num_add_args)
+def func(
+          x: ShapedTensor["a b c"],
+          y: ShapedTensor["b d"],
+          *args: tuple[ShapedTensor["10 5"], ...]
+) -> ShapedTensor["a d"]:
+    z = x[:, :, :, None] * y[None, :, None]
+    return z.sum(axis=(1, 2)) # type: ignore
 
-        # print results and compute percentage 
 
-        print(f"\nBenchmarking with {num_add_args} additional arguments:")
-        print(f"Duration without annotations:        {duration_not_annotated*1000:.4f} ms, 100.00%")
-        print(f"Duration with global check never:    {duration_global_check_never*1000:.4f} ms, {duration_global_check_never/duration_not_annotated*100:5.2f}%")
-        print(f"Duration with global check once:     {duration_global_check_once*1000:.4f} ms, {duration_global_check_once/duration_not_annotated*100:5.2f}%")
-        print(f"Duration with shape checking:        {duration_with*1000:.4f} ms, {duration_with/duration_not_annotated*100:5.2f}%")
-        print("-" * 50)
\ No newline at end of file
+if __name__ == "__main__":
+    func_always = check_tensor_shapes(check_mode="always")(func)
+    func_once = check_tensor_shapes(check_mode="once")(func)
+    func_never = check_tensor_shapes(check_mode="never")(func)
+
+    results = []
+
+    def add_results(lib, num_add_args, mode, duration):
+
+        if mode == "not_annotated":
+            duration_not_annotated = duration
+        else:
+            duration_not_annotated = None
+            for r in results:
+                if r["library"] == lib and r["additional args"] == num_add_args and r["check mode"] == "not_annotated":
+                    duration_not_annotated = r["duration (ms)"] / 1000
+                    break
+
+        results.append({
+            "library": lib,
+            "additional args": num_add_args,
+            "check mode": mode,
+            "duration (ms)": duration * 1000,
+            "overhead (ms)": (duration - duration_not_annotated) * 1000,
+            "relative (%)": duration / duration_not_annotated
+        })
+
+    for lib in NAME_LIBRARY_MAP.keys():
+        try:
+            xp = get_library_by_name(lib)
+        except ModuleNotFoundError:
+            print("skipping", lib, ", not installed")
+            continue
+
+        try:
+            for num_add_args in (0, 10, 100):
+
+                add_results(lib, num_add_args, "not_annotated", benchmark(func, num_additonal_args=num_add_args, xp=xp))
+                add_results(lib, num_add_args, "never", benchmark(func_never, num_additonal_args=num_add_args, xp=xp))
+                add_results(lib, num_add_args, "once", benchmark(func_once, num_additonal_args=num_add_args, xp=xp))
+                add_results(lib, num_add_args, "always", benchmark(func_always, num_additonal_args=num_add_args, xp=xp))
+
+                # if lib == "torch":
+                #     import torch
+                #     add_results("torch-compile", num_add_args, "not_annotated", benchmark(torch.compile(func), num_additonal_args=num_add_args, xp=xp))
+                #     add_results("torch-compile", num_add_args, "never", benchmark(torch.compile(func_never), num_additonal_args=num_add_args, xp=xp))
+                #     add_results("torch-compile", num_add_args, "once", benchmark(torch.compile(func_once), num_additonal_args=num_add_args, xp=xp))
+                #     add_results("torch-compile", num_add_args, "always", benchmark(torch.compile(func_always), num_additonal_args=num_add_args, xp=xp))
+
+                print(tabulate(results, tablefmt="github", floatfmt=(None, None, None, ".5f", ".4e", ".2%"))) # type: ignore
+        except Exception as e:
+            print("Error benchmarking", lib, ":", e)
+
+    # write results to csv file
+
+    with open("benchmark_results.csv", "w") as f:
+        f.write("library,additional args,check mode,duration (ms),relative (%),overhead (ms)\n")
+        for r in results:
+            f.write(f"{r['library']},{r['additional args']},{r['check mode']},{r['duration (ms)']:.5f},{r['relative (%)']:.2%},{r['overhead (ms)']:.10f}\n")
diff --git a/speedtest_plot.ipynb b/speedtest_plot.ipynb
new file mode 100644
index 0000000..7f54d15
--- /dev/null
+++ b/speedtest_plot.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c9b03cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# read benchmark results from CSV\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tabulate import tabulate\n",
+    "\n",
+    "df = pd.read_csv(\"benchmark_results.csv\")\n",
+    "\n",
+    "# plot benchmark results\n",
+    "\n",
+    "modes = df['check mode'].unique()\n",
+    "libs = df['library'].unique()\n",
+    "n_libs = len(libs)\n",
+    "\n",
+    "# find a small positive epsilon to replace non-positive values (log scale can't handle <= 0)\n",
+    "pos_min = df[df['overhead (ms)'] > 0]['overhead (ms)'].min()\n",
+    "epsilon = 1e-4\n",
+    "\n",
+    "for add_args in df['additional args'].unique():\n",
+    "    x = np.arange(len(modes))\n",
+    "    width = 0.8 / n_libs\n",
+    "\n",
+    "    for i, lib in enumerate(libs):\n",
+    "        lib_df = df[(df['library'] == lib) & (df['additional args'] == add_args)]\n",
+    "        vals = lib_df.set_index('check mode').reindex(modes)['overhead (ms)']\n",
+    "        vals_plot = vals.copy().astype(float).where(vals > 0, epsilon)  # replace <=0 with epsilon\n",
+    "        plt.bar(x + i * width, vals_plot, width=width, label=lib)\n",
+    "\n",
+    "    plt.xticks(x + width * (n_libs - 1) / 2, modes)\n",
+    "    plt.xlabel(\"Check Mode\")\n",
+    "    plt.ylabel(\"Overhead (ms)\")\n",
+    "    plt.yscale('log')\n",
+    "    plt.title(f\"Decorator Overhead (additional args = {add_args})\")\n",
+    "    plt.legend()\n",
+    "    plt.savefig(f\"assets/benchmark_overhead_additional_args_{add_args}.png\")\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "8c99f086",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   id | lib    |   num_args | method        |    s/it | rel     |   overhead |\n",
+      "|------|--------|------------|---------------|---------|---------|------------|\n",
+      "|    8 | torch  |        100 | not_annotated | 0.02975 | 100.00% |    0.0e+00 |\n",
+      "|    9 | torch  |        100 | never         | 0.03106 | 104.39% |    1.3e-06 |\n",
+      "|   10 | torch  |        100 | once          | 0.03391 | 113.96% |    4.2e-06 |\n",
+      "|   11 | torch  |        100 | always        | 0.28250 | 949.49% |    2.5e-04 |\n",
+      "|   20 | numpy  |        100 | not_annotated | 0.15465 | 100.00% |    0.0e+00 |\n",
+      "|   21 | numpy  |        100 | never         | 0.15588 | 100.79% |    1.2e-06 |\n",
+      "|   22 | numpy  |        100 | once          | 0.15839 | 102.42% |    3.7e-06 |\n",
+      "|   23 | numpy  |        100 | always        | 0.38753 | 250.59% |    2.3e-04 |\n",
+      "|   32 | dask   |        100 | not_annotated | 1.50759 | 100.00% |    0.0e+00 |\n",
+      "|   33 | dask   |        100 | never         | 1.50902 | 100.09% |    1.4e-06 |\n",
+      "|   34 | dask   |        100 | once          | 1.51694 | 100.62% |    9.4e-06 |\n",
+      "|   35 | dask   |        100 | always        | 1.79947 | 119.36% |    2.9e-04 |\n",
+      "|   44 | jax    |        100 | not_annotated | 0.32844 | 100.00% |    0.0e+00 |\n",
+      "|   45 | jax    |        100 | never         | 0.33278 | 101.32% |    4.3e-06 |\n",
+      "|   46 | jax    |        100 | once          | 0.33702 | 102.61% |    8.6e-06 |\n",
+      "|   47 | jax    |        100 | always        | 0.60768 | 185.02% |    2.8e-04 |\n",
+      "|   56 | sparse |        100 | not_annotated | 0.57341 | 100.00% |    0.0e+00 |\n",
+      "|   57 | sparse |        100 | never         | 0.57755 | 100.72% |    4.1e-06 |\n",
+      "|   58 | sparse |        100 | once          | 0.58779 | 102.51% |    1.4e-05 |\n",
+      "|   59 | sparse |        100 | always        | 0.84208 | 146.86% |    2.7e-04 |\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"benchmark_results.csv\")\n",
+    "print(tabulate(df[df[\"additional args\"] == 100], tablefmt=\"github\", floatfmt=(None, None, None, None, \".5f\", \".2%\", \".1e\"), headers=[\"id\", \"lib\", \"num_args\", \"method\", \"s/it\", \"rel\", \"overhead\"])) # type: ignore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tsa-test-p312",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/tensor_shape_assert/__init__.py b/src/tensor_shape_assert/__init__.py
index 8af932d..434d47c 100644
--- a/src/tensor_shape_assert/__init__.py
+++ b/src/tensor_shape_assert/__init__.py
@@ -2,5 +2,5 @@
     check_tensor_shapes, get_shape_variables, assert_shape_here,
     set_global_check_mode
 )
-from .types import ShapedTensor, ShapedTorchLiteral, ShapedNumpyLiteral
+from .types import ShapedTensor, ShapedTorchLiteral, ShapedNumpyLiteral, ShapedLiteral
 from .types import ScalarTensor  # type: ignore
\ No newline at end of file
diff --git a/src/tensor_shape_assert/types.py b/src/tensor_shape_assert/types.py
index 3126a27..6ce315f 100644
--- a/src/tensor_shape_assert/types.py
+++ b/src/tensor_shape_assert/types.py
@@ -118,6 +118,15 @@ def __init__(self, *args, **kwargs):
         )
     
     def __class_getitem__(cls, key):
+        # check if it is a tuple annotation
+        if isinstance(key, tuple):
+            if len(key) != 2:
+                raise TypeError(
+                    "ShapedTensor can only be parameterized with a single "
+                    "shape descriptor string or a tuple of (type, shape)."
+                )
+            key = key[1]
+
         # check if it is a literal
         if get_origin(key) is Literal:
             key = " ".join(get_args(key))
@@ -138,7 +147,7 @@ def __class_getitem__(cls, key):
     # torch
 
     try:
-        from array_api_compat import torch
+        import torch
         ShapedTorchLiteral = TypeAliasType(
             'ShapedTorchLiteral',
             ShapedLiteral[torch.Tensor, S],
@@ -150,7 +159,7 @@ def __class_getitem__(cls, key):
     # numpy
 
     try:
-        from array_api_compat import numpy
+        import numpy
         ShapedNumpyLiteral = TypeAliasType(
             'ShapedNumpyLiteral',
             ShapedLiteral[numpy.ndarray, S],
diff --git a/src/tensor_shape_assert_test.py b/src/tensor_shape_assert_test.py
index 4938214..a74a59d 100644
--- a/src/tensor_shape_assert_test.py
+++ b/src/tensor_shape_assert_test.py
@@ -2,6 +2,7 @@
 # pyright: reportReturnType=false
 # pyright: reportAttributeAccessIssue=false
 # pyright: reportOperatorIssue=false
+# pyright: reportPossiblyUnboundVariable=false
 # mypy: ignore-errors
 
 import sys
@@ -31,27 +32,10 @@
     NoVariableContextExistsError,
     VariableConstraintError
 )
+from test_utils import get_library_by_name
 
 # read library to be used from env
-lib = os.environ["TSA_TEST_LIBRARY"]
-
-
-if lib == "torch" or TYPE_CHECKING:
-    import array_api_compat.torch as xp
-elif lib == "numpy":
-    import array_api_compat.numpy as xp
-elif lib == "cupy":
-    import array_api_compat.cupy as xp
-elif lib == "dask":
-    import array_api_compat.dask.array as xp
-elif lib == "jax":
-    import jax.numpy as xp
-elif lib == "ndonnx":
-    import ndonnx as xp
-elif lib == "sparse":
-    import sparse as xp
-else:
-    raise ValueError(f"Unsupported library: {lib}")
+xp = get_library_by_name(os.environ["TSA_TEST_LIBRARY"])
 
 
 def library_has_types(type_names: list[str]) -> bool:
diff --git a/src/tensor_shape_assert_typesafe_test.py b/src/tensor_shape_assert_typesafe_test.py
index 9a4c272..22aacea 100644
--- a/src/tensor_shape_assert_typesafe_test.py
+++ b/src/tensor_shape_assert_typesafe_test.py
@@ -5,50 +5,35 @@
     ShapedLiteral,
     ShapedNumpyLiteral,
     ShapedTorchLiteral,
+    ShapedTensor
 )
 from tensor_shape_assert.wrapper import (
     check_tensor_shapes,
 )
 from tensor_shape_assert.utils import TensorShapeAssertError
-
+from typing_extensions import Literal as L
 
 # read library to be used from env
 lib = os.environ["TSA_TEST_LIBRARY"]
 
 
-if lib == "numpy" or TYPE_CHECKING:
-    import array_api_compat.numpy as xp
-elif lib == "torch":
-    import array_api_compat.torch as xp
-elif lib == "cupy":
-    import array_api_compat.cupy as xp
-elif lib == "dask":
-    import array_api_compat.dask.array as xp
-elif lib == "jax":
-    import jax.numpy as xp
-elif lib == "ndonnx":
-    import ndonnx as xp
-elif lib == "sparse":
-    import sparse as xp
-else:
-    raise ValueError(f"Unsupported library: {lib}")
-
-
 class TestAnnotationWithLiterals(unittest.TestCase):
-    # def test_literal_annotation_torch(self):
-    #     if lib != "torch":
-    #         self.skipTest("Skipping torch-specific test")
+    def test_literal_annotation_torch(self):
+        if lib != "torch":
+            self.skipTest("Skipping torch-specific test")
 
-    #     from typing_extensions import Literal as L
-    #     import torch
+        from typing_extensions import Literal as L
+        import torch
         
-    #     @check_tensor_shapes()
-    #     def test(x: ShapedLiteral[torch.Tensor, L["3 a 5"]]) -> ShapedLiteral[torch.Tensor, L["a 5"]]:
-    #         return torch.sum(x, dim=0)
+        @check_tensor_shapes()
+        def test(
+                x: ShapedLiteral[torch.Tensor, L["3 a 5"]]
+        ) -> ShapedLiteral[torch.Tensor, L["a 5"]]:
+            return torch.sum(x, dim=0)
 
-    #     test(x=torch.zeros((3, 4, 5)))
-    #     with self.assertRaises(TensorShapeAssertError):
-    #         test(x=torch.zeros((2, 4, 5)))
+        test(x=torch.zeros((3, 4, 5)))
+        with self.assertRaises(TensorShapeAssertError):
+            test(x=torch.zeros((2, 4, 5)))
 
     def test_literal_annotation_torch_alias(self):
         if lib != "torch":
@@ -58,27 +43,32 @@ def test_literal_annotation_torch_alias(self):
         import torch
         
         @check_tensor_shapes()
-        def test(x: ShapedTorchLiteral[L["3 a 5"]]) -> ShapedTorchLiteral[L["a 5"]]:
+        def test(
+                x: ShapedTorchLiteral[L["3 a 5"]]
+        ) -> ShapedTorchLiteral[L["a 5"]]:
             return torch.sum(x, dim=0)
 
         test(x=torch.zeros((3, 4, 5)))
         with self.assertRaises(TensorShapeAssertError):
             test(x=torch.zeros((2, 4, 5)))
 
-    # def test_literal_annotation_numpy(self):
-    #     if lib != "numpy":
-    #         self.skipTest("Skipping numpy-specific test")
+    def test_literal_annotation_numpy(self):
+        if lib != "numpy":
+            self.skipTest("Skipping numpy-specific test")
 
-    #     from typing_extensions import Literal as L
-    #     import numpy as np
+        from typing_extensions import Literal as L
+        import numpy as np
         
-    #     @check_tensor_shapes()
-    #     def test(x: ShapedLiteral[np.ndarray, L["3 a 5"]]) -> ShapedLiteral[np.ndarray, L["a 5"]]:
-    #         return np.sum(x, axis=0)
+        @check_tensor_shapes()
+        def test(
+                x: ShapedLiteral[np.ndarray, L["3 a 5"]]
+        ) -> ShapedLiteral[np.ndarray, L["a 5"]]:
+            return np.sum(x, axis=0)
+
+        test(x=np.zeros((3, 4, 5)))
+        with self.assertRaises(TensorShapeAssertError):
+            test(x=np.zeros((2, 4, 5)))
 
-    #     test(x=np.zeros((3, 4, 5)))
-    #     with self.assertRaises(TensorShapeAssertError):
-    #         test(x=np.zeros((2, 4, 5)))
 
     def test_literal_annotation_numpy_alias(self):
         if lib != "numpy":
@@ -88,7 +78,9 @@ def test_literal_annotation_numpy_alias(self):
         import numpy as np
         
         @check_tensor_shapes()
-        def test(x: ShapedNumpyLiteral[L["3 a 5"]]) -> ShapedNumpyLiteral[L["a 5"]]:
+        def test(
+                x: ShapedNumpyLiteral[L["3 a 5"]]
+        ) -> ShapedNumpyLiteral[L["a 5"]]:
             return np.sum(x, axis=0)
 
         test(x=np.zeros((3, 4, 5)))
diff --git a/src/test_utils.py b/src/test_utils.py
new file mode 100644
index 0000000..a474da8
--- /dev/null
+++ b/src/test_utils.py
@@ -0,0 +1,19 @@
+from types import ModuleType
+from importlib import import_module
+
+NAME_LIBRARY_MAP = {
+    "torch": "array_api_compat.torch",
+    "numpy": "array_api_compat.numpy",
+    "cupy": "array_api_compat.cupy",
+    "dask": "array_api_compat.dask.array",
+    "jax": "jax.numpy",
+    "ndonnx": "ndonnx",
+    "sparse": "sparse",
+}
+
+def get_library_by_name(name: str) -> ModuleType:
+    try:
+        module_name = NAME_LIBRARY_MAP[name]
+        return import_module(module_name)
+    except KeyError:
+        raise ValueError(f"Unsupported library: {name}")
\ No newline at end of file