[pre-commit.ci] pre-commit autoupdate

.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

.pre-commit-config.yaml

@@ -8,14 +8,14 @@ default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.9
+    rev: v0.12.2
     hooks:
       - id: ruff
         args: [--fix, --ignore, D]
       - id: ruff-format
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.15.0
+    rev: v1.16.1
     hooks:
       - id: mypy
 

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)"
    ]
   },
@@ -329,11 +329,11 @@
     "    # 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 +477,7 @@
     "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 +506,7 @@
     "    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",

pyproject.toml

@@ -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",
@@ -68,7 +68,7 @@ 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]

tests/test_flows.py

@@ -1,4 +1,4 @@
-from typing import Sequence
+from collections.abc import Sequence
 
 import torch
 from torch import Tensor, nn

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

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

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

torch_mnf/layers/made.py

@@ -41,7 +41,7 @@ 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 +89,5 @@ 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)

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

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

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)]
             )

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