bump min supported python version from 3.8 to 3.11

janosh · janosh · commit fe45b14bf39a · 2025-11-03T08:45:54.000-08:00
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
@@ -0,0 +1,17 @@
+name: Lint
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  prek:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repo
+        uses: actions/checkout@v5
+
+      - name: Run prek
+        uses: j178/prek-action@v1
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -11,5 +11,5 @@ jobs:
   tests:
     uses: janosh/workflows/.github/workflows/pytest.yml@main
     with:
-      python-version: "3.10"
+      python-version: "3.11"
     secrets: inherit
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,31 +1,21 @@
-ci:
-  autoupdate_schedule: quarterly
-  skip: [ty]
-
 default_stages: [pre-commit]
-
 default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.9
+    rev: v0.14.3
     hooks:
-      - id: ruff
+      - id: ruff-check
         args: [--fix, --ignore, D]
       - id: ruff-format
 
-  - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.15.0
-    hooks:
-      - id: mypy
-
   - repo: https://github.com/janosh/format-ipy-cells
     rev: v0.1.11
     hooks:
       - id: format-ipy-cells
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: check-case-conflict
       - id: check-symlinks
@@ -40,6 +30,7 @@ repos:
     rev: v2.4.1
     hooks:
       - id: codespell
+        stages: [pre-commit, commit-msg]
         exclude_types: [jupyter]
         args: [--check-filenames]
 
@@ -53,5 +44,6 @@ repos:
     hooks:
       - id: ty
         name: ty check
-        entry: ty check .
+        entry: ty check
         language: python
+        additional_dependencies: [ty]
diff --git a/examples/half_moons.ipynb b/examples/half_moons.ipynb
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Callable\n",
+    "from collections.abc import Callable\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -245,7 +245,7 @@
     }
    ],
    "source": [
-    "steps, loss_vals = zip(*losses.items())\n",
+    "steps, loss_vals = zip(*losses.items(), strict=True)\n",
     "plt.plot(steps, loss_vals)"
    ]
   },
@@ -321,19 +321,23 @@
    "outputs": [],
    "source": [
     "def plot_grid_warp(\n",
-    "    ax: plt.Axes, z: np.ndarray, target_samples: np.ndarray, n_lines: int, idx: int\n",
+    "    ax: plt.Axes,\n",
+    "    z: np.ndarray,\n",
+    "    target_samples: np.ndarray | torch.Tensor,\n",
+    "    n_lines: int,\n",
+    "    idx: int,\n",
     "):\n",
     "    \"\"\"plots how the flow warps space\"\"\"\n",
     "\n",
     "    grid = z.reshape((n_lines, n_lines, 2))\n",
     "    # y coords\n",
     "    p1 = np.reshape(grid[1:, :, :], (n_lines**2 - n_lines, 2))\n",
     "    p2 = np.reshape(grid[:-1, :, :], (n_lines**2 - n_lines, 2))\n",
-    "    lcy = LineCollection(tuple(zip(p1, p2)), alpha=0.3)\n",
+    "    lcy = LineCollection(tuple(zip(p1, p2, strict=True)), alpha=0.3)\n",
     "    # x coords\n",
     "    p1 = np.reshape(grid[:, 1:, :], (n_lines**2 - n_lines, 2))\n",
     "    p2 = np.reshape(grid[:, :-1, :], (n_lines**2 - n_lines, 2))\n",
-    "    lcx = LineCollection(tuple(zip(p1, p2)), alpha=0.3)\n",
+    "    lcx = LineCollection(tuple(zip(p1, p2, strict=True)), alpha=0.3)\n",
     "    # draw the lines\n",
     "    ax.add_collection(lcx)\n",
     "    ax.add_collection(lcy)\n",
@@ -477,7 +481,7 @@
     "xs, *_ = model.forward(latent_grid)\n",
     "xs = [z.detach().numpy() for z in xs]\n",
     "\n",
-    "for idx, [z0, z1] in enumerate(zip(xs, xs[1:])):\n",
+    "for idx, [z0, z1] in enumerate(zip(xs, xs[1:], strict=True)):\n",
     "    _, [ax1, ax2] = plt.subplots(1, 2, figsize=(10, 5))\n",
     "\n",
     "    plot_point_flow(ax1, z0, z1)\n",
@@ -506,7 +510,7 @@
     "    fig, axes = plt.subplots(plot_grid_height, 2 * plot_grid_height, figsize=(20, 10))\n",
     "    fig.subplots_adjust(wspace=0.05, hspace=0.05)\n",
     "\n",
-    "    for z0, z1, ax in zip(xs, xs[1:], axes[:, :plot_grid_height].flat):\n",
+    "    for z0, z1, ax in zip(xs, xs[1:], axes[:, :plot_grid_height].flat, strict=True):\n",
     "        plot_point_flow(ax, z0, z1)\n",
     "        ax.set(xlim=[-4, 4], ylim=[-4, 4], xticks=[], yticks=[])\n",
     "\n",
diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [build-system]
-requires = ["setuptools>=61.2"]
-build-backend = "setuptools.build_meta"
+requires = ["uv_build>=0.9.5"]
+build-backend = "uv_build"
 
 [project]
 name = "torch-mnf"
@@ -21,13 +21,13 @@ classifiers = [
     "License :: OSI Approved :: MIT License",
     "Programming Language :: Python :: 3 :: Only",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
 ]
 urls = { Homepage = "https://github.com/janosh/torch-mnf" }
-requires-python = ">=3.8"
+requires-python = ">=3.11"
 dependencies = [
     "matplotlib",
     "numpy",
@@ -42,14 +42,10 @@ dependencies = [
 [project.optional-dependencies]
 test = ["pytest", "pytest-cov"]
 
-[tool.setuptools.packages]
-find = {}
-
-[tool.setuptools.package-data]
-torch_mnf = ["*.csv"]
-
-[tool.distutils.bdist_wheel]
-universal = true
+[tool.uv.build-backend]
+module-name = "torch_mnf"
+module-root = ""
+source-include = ["torch_mnf/**/*.csv"]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
@@ -68,7 +64,7 @@ no_implicit_optional = false
 ignore-words-list = "hist"
 
 [tool.ruff]
-target-version = "py38"
+target-version = "py311"
 include = ["**/pyproject.toml", "*.ipynb", "*.py", "*.pyi"]
 
 [tool.ruff.lint]
diff --git a/readme.md b/readme.md
@@ -1,7 +1,6 @@
 # Torch MNF
 
 [![Tests](https://github.com/janosh/torch-mnf/actions/workflows/test.yml/badge.svg)](https://github.com/janosh/torch-mnf/actions/workflows/test.yml)
-[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/janosh/torch-mnf/master.svg)](https://results.pre-commit.ci/latest/github/janosh/torch-mnf/master)
 ![GitHub Repo Size](https://img.shields.io/github/repo-size/janosh/torch-mnf?label=Repo+Size)
 
 PyTorch implementation of Multiplicative Normalizing Flows [[1]](#mnf-bnn).
diff --git a/tests/test_flows.py b/tests/test_flows.py
@@ -1,4 +1,4 @@
-from typing import Sequence
+from collections.abc import Sequence
 
 import torch
 from torch import Tensor, nn
diff --git a/torch_mnf/flows/affine_half_flow.py b/torch_mnf/flows/affine_half_flow.py
@@ -9,7 +9,7 @@
 (Laurent's extension of NICE)
 """
 
-from typing import Sequence
+from collections.abc import Sequence
 
 import torch
 from torch import Tensor, nn
diff --git a/torch_mnf/flows/core.py b/torch_mnf/flows/core.py
@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Sequence
+from collections.abc import Sequence
 
 import torch
 from torch import Tensor, nn
@@ -52,4 +52,4 @@ def sample(self, *num_samples: int) -> Tensor:
         """Sample from the flow's base distribution."""
         z = self.base.sample(*num_samples)
         xs, _ = self.forward(z)
-        return xs
+        return xs[-1]
diff --git a/torch_mnf/flows/maf.py b/torch_mnf/flows/maf.py
@@ -10,7 +10,7 @@
 
 from __future__ import annotations
 
-from typing import Sequence
+from collections.abc import Sequence
 
 import torch
 from torch import nn
diff --git a/torch_mnf/layers/made.py b/torch_mnf/layers/made.py
@@ -15,8 +15,9 @@ def __init__(self, n_in, n_out, bias=True):
         super().__init__(n_in, n_out, bias)
         self.register_buffer("mask", torch.ones(n_in, n_out))
 
-    def set_mask(self, mask):
-        self.mask.data.copy_(torch.from_numpy(mask))
+    def set_mask(self, mask: np.ndarray) -> None:
+        """Set the mask for this layer."""
+        self.mask = torch.from_numpy(mask)
 
     def forward(self, x):
         return x @ (self.weight.T * self.mask) + self.bias
@@ -41,7 +42,7 @@ def __init__(self, n_in, hidden_sizes, n_out, num_masks=1, natural_ordering=Fals
         # define a simple MLP neural net
         layers = []
         hs = [n_in, *list(hidden_sizes), n_out]
-        for h0, h1 in zip(hs, hs[1:]):
+        for h0, h1 in zip(hs, hs[1:], strict=False):
             layers.extend([MaskedLinear(h0, h1), nn.ReLU()])
         super().__init__(*layers[:-1])  # drop last ReLU)
 
@@ -89,5 +90,5 @@ def update_masks(self):
 
         # set the masks in all MaskedLinear layers
         masked_layers = [lyr for lyr in self if isinstance(lyr, MaskedLinear)]
-        for lyr, m in zip(masked_layers, masks):
+        for lyr, m in zip(masked_layers, masks, strict=False):
             lyr.set_mask(m)
diff --git a/torch_mnf/layers/mnf_conv.py b/torch_mnf/layers/mnf_conv.py
@@ -1,4 +1,4 @@
-from typing import Sequence
+from collections.abc import Sequence
 
 import torch
 import torch.nn.functional as F
diff --git a/torch_mnf/models/mlp.py b/torch_mnf/models/mlp.py
@@ -6,7 +6,7 @@ class MLP(nn.Sequential):
 
     def __init__(self, *layer_sizes, leaky_a=0.2):
         layers = []
-        for s1, s2 in zip(layer_sizes, layer_sizes[1:]):
+        for s1, s2 in zip(layer_sizes, layer_sizes[1:], strict=False):
             layers.append(nn.Linear(s1, s2))
             layers.append(nn.LeakyReLU(leaky_a))
         super().__init__(*layers[:-1])  # drop last ReLU
diff --git a/torch_mnf/models/mnf_feed_forward.py b/torch_mnf/models/mnf_feed_forward.py
@@ -1,4 +1,5 @@
-from typing import Any, Sequence
+from collections.abc import Sequence
+from typing import Any
 
 from torch import nn
 from torch.nn import BatchNorm1d, ReLU, Sequential
@@ -23,7 +24,7 @@ def __init__(
     ) -> None:
         """Initialize the model."""
         layers = []
-        for s1, s2 in zip(layer_sizes, layer_sizes[1:]):
+        for s1, s2 in zip(layer_sizes, layer_sizes[1:], strict=False):
             layers.extend(
                 [MNFLinear()(s1, s2, **kwargs), activation(), BatchNorm1d(s2)]
             )
@@ -34,4 +35,4 @@ def kl_div(self) -> float:
         as a regularization term in the loss function. Tensorflow will issue
         warnings "Gradients do not exist for variables of MNFLinear" if you forget.
         """
-        return sum(lyr.kl_div() for lyr in self.layers if hasattr(lyr, "kl_div"))
+        return sum(lyr.kl_div() for lyr in self if hasattr(lyr, "kl_div"))
diff --git a/torch_mnf/utils.py b/torch_mnf/utils.py
@@ -1,8 +1,8 @@
 from __future__ import annotations
 
 import os
+from collections.abc import Callable
 from functools import wraps
-from typing import Callable
 
 import matplotlib.pyplot as plt
 import pandas as pd

Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-from typing import Sequence`
	`1`	`+from collections.abc import Sequence`
`2`	`2`
`3`	`3`	`import torch`
`4`	`4`	`from torch import Tensor, nn`