precommit: ruff-format (#19434)

* precommit: ruff-format

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* manual update

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* manual update

* order

* mypy

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* mypy

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: Borda

.pre-commit-config.yaml

@@ -84,10 +84,12 @@ repos:
           - flake8-return
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.15"
+    rev: "v0.2.0"
     hooks:
       - id: ruff
         args: ["--fix", "--preview"]
+      - id: ruff-format
+        args: ["--preview"]
 
   - repo: https://github.com/executablebooks/mdformat
     rev: 0.7.17

examples/app/dag/app.py

@@ -65,9 +65,9 @@ def __init__(self, models_paths: list):
         )
 
         # Step 3: Create the work to train the models_paths in parallel.
-        self.dict = Dict(
-            **{model_path.split(".")[-1]: ModelWork(model_path, parallel=True) for model_path in models_paths}
-        )
+        self.dict = Dict(**{
+            model_path.split(".")[-1]: ModelWork(model_path, parallel=True) for model_path in models_paths
+        })
 
         # Step 4: Some element to track components progress.
         self.has_completed = False

examples/app/server/app.py

@@ -20,13 +20,11 @@ def setup(self):
     def predict(self, request):
         image = base64.b64decode(request.image.encode("utf-8"))
         image = Image.open(io.BytesIO(image))
-        transforms = torchvision.transforms.Compose(
-            [
-                torchvision.transforms.Resize(224),
-                torchvision.transforms.ToTensor(),
-                torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-            ]
-        )
+        transforms = torchvision.transforms.Compose([
+            torchvision.transforms.Resize(224),
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ])
         image = transforms(image)
         image = image.to(self._device)
         prediction = self._model(image.unsqueeze(0))

examples/app/server_with_auto_scaler/app.py

@@ -34,13 +34,11 @@ def setup(self):
         self._model = torchvision.models.resnet18(pretrained=True).to(self._device)
 
     def predict(self, requests: BatchRequestModel):
-        transforms = torchvision.transforms.Compose(
-            [
-                torchvision.transforms.Resize(224),
-                torchvision.transforms.ToTensor(),
-                torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-            ]
-        )
+        transforms = torchvision.transforms.Compose([
+            torchvision.transforms.Resize(224),
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ])
         images = []
         for request in requests.inputs:
             image = app.components.serve.types.image.Image.deserialize(request.image)

examples/fabric/dcgan/train_fabric.py

@@ -4,6 +4,7 @@
 Code adapted from the official PyTorch DCGAN tutorial:
 https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
 """
+
 import os
 import time
 from pathlib import Path
@@ -55,14 +56,12 @@ def main():
         root=dataroot,
         split="all",
         download=True,
-        transform=transforms.Compose(
-            [
-                transforms.Resize(image_size),
-                transforms.CenterCrop(image_size),
-                transforms.ToTensor(),
-                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-            ]
-        ),
+        transform=transforms.Compose([
+            transforms.Resize(image_size),
+            transforms.CenterCrop(image_size),
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+        ]),
     )
 
     # Create the dataloader
@@ -227,7 +226,7 @@ def __init__(self):
             nn.ReLU(True),
             # state size. (ngf) x 32 x 32
             nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
-            nn.Tanh()
+            nn.Tanh(),
             # state size. (nc) x 64 x 64
         )
 

examples/fabric/dcgan/train_torch.py

@@ -4,6 +4,7 @@
 Code adapted from the official PyTorch DCGAN tutorial:
 https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
 """
+
 import os
 import random
 import time
@@ -55,14 +56,12 @@ def main():
         root=dataroot,
         split="all",
         download=True,
-        transform=transforms.Compose(
-            [
-                transforms.Resize(image_size),
-                transforms.CenterCrop(image_size),
-                transforms.ToTensor(),
-                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-            ]
-        ),
+        transform=transforms.Compose([
+            transforms.Resize(image_size),
+            transforms.CenterCrop(image_size),
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+        ]),
     )
 
     # Create the dataloader
@@ -236,7 +235,7 @@ def __init__(self):
             nn.ReLU(True),
             # state size. (ngf) x 32 x 32
             nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
-            nn.Tanh()
+            nn.Tanh(),
             # state size. (nc) x 64 x 64
         )
 

examples/fabric/meta_learning/train_fabric.py

@@ -14,6 +14,7 @@
 Run it with:
     lightning run model train_fabric.py --accelerator=cuda --devices=2 --strategy=ddp
 """
+
 import cherry
 import learn2learn as l2l
 import torch

examples/fabric/meta_learning/train_torch.py

@@ -15,6 +15,7 @@
 Run it with:
     torchrun --nproc_per_node=2 --standalone train_torch.py
 """
+
 import os
 import random
 

examples/fabric/reinforcement_learning/train_fabric.py

@@ -84,14 +84,10 @@ def main(args: argparse.Namespace):
     )
 
     # Environment setup
-    envs = gym.vector.SyncVectorEnv(
-        [
-            make_env(
-                args.env_id, args.seed + rank * args.num_envs + i, rank, args.capture_video, logger.log_dir, "train"
-            )
-            for i in range(args.num_envs)
-        ]
-    )
+    envs = gym.vector.SyncVectorEnv([
+        make_env(args.env_id, args.seed + rank * args.num_envs + i, rank, args.capture_video, logger.log_dir, "train")
+        for i in range(args.num_envs)
+    ])
     assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
 
     # Define the agent and the optimizer and setup them with Fabric

examples/fabric/reinforcement_learning/train_fabric_decoupled.py

@@ -59,9 +59,9 @@ def player(args, world_collective: TorchCollective, player_trainer_collective: T
     )
 
     # Environment setup
-    envs = gym.vector.SyncVectorEnv(
-        [make_env(args.env_id, args.seed + i, 0, args.capture_video, log_dir, "train") for i in range(args.num_envs)]
-    )
+    envs = gym.vector.SyncVectorEnv([
+        make_env(args.env_id, args.seed + i, 0, args.capture_video, log_dir, "train") for i in range(args.num_envs)
+    ])
     assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
 
     # Define the agent

examples/fabric/reinforcement_learning/train_torch.py

@@ -142,19 +142,17 @@ def main(args: argparse.Namespace):
         )
 
     # Environment setup
-    envs = gym.vector.SyncVectorEnv(
-        [
-            make_env(
-                args.env_id,
-                args.seed + global_rank * args.num_envs + i,
-                global_rank,
-                args.capture_video,
-                logger.log_dir if global_rank == 0 else None,
-                "train",
-            )
-            for i in range(args.num_envs)
-        ]
-    )
+    envs = gym.vector.SyncVectorEnv([
+        make_env(
+            args.env_id,
+            args.seed + global_rank * args.num_envs + i,
+            global_rank,
+            args.capture_video,
+            logger.log_dir if global_rank == 0 else None,
+            "train",
+        )
+        for i in range(args.num_envs)
+    ])
     assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
 
     # Define the agent and the optimizer and setup them with DistributedDataParallel

examples/pytorch/basics/autoencoder.py

@@ -16,6 +16,7 @@
 To run: python autoencoder.py --trainer.max_epochs=50
 
 """
+
 from os import path
 from typing import Optional, Tuple
 

examples/pytorch/basics/backbone_image_classifier.py

@@ -16,6 +16,7 @@
 To run: python backbone_image_classifier.py --trainer.max_epochs=50
 
 """
+
 from os import path
 from typing import Optional
 

examples/pytorch/domain_templates/computer_vision_fine_tuning.py

@@ -119,14 +119,12 @@ def normalize_transform(self):
 
     @property
     def train_transform(self):
-        return transforms.Compose(
-            [
-                transforms.Resize((224, 224)),
-                transforms.RandomHorizontalFlip(),
-                transforms.ToTensor(),
-                self.normalize_transform,
-            ]
-        )
+        return transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            self.normalize_transform,
+        ])
 
     @property
     def valid_transform(self):
@@ -269,13 +267,11 @@ def add_arguments_to_parser(self, parser):
         parser.link_arguments("data.batch_size", "model.batch_size")
         parser.link_arguments("finetuning.milestones", "model.milestones")
         parser.link_arguments("finetuning.train_bn", "model.train_bn")
-        parser.set_defaults(
-            {
-                "trainer.max_epochs": 15,
-                "trainer.enable_model_summary": False,
-                "trainer.num_sanity_val_steps": 0,
-            }
-        )
+        parser.set_defaults({
+            "trainer.max_epochs": 15,
+            "trainer.enable_model_summary": False,
+            "trainer.num_sanity_val_steps": 0,
+        })
 
 
 def cli_main():

examples/pytorch/domain_templates/generative_adversarial_net.py

@@ -18,6 +18,7 @@
 tensorboard --logdir default
 
 """
+
 from argparse import ArgumentParser, Namespace
 
 import numpy as np

examples/pytorch/domain_templates/imagenet.py

@@ -30,6 +30,7 @@
     python imagenet.py fit --help
 
 """
+
 import os
 from typing import Optional
 
@@ -139,14 +140,12 @@ def setup(self, stage: str):
             train_dir = os.path.join(self.data_path, "train")
             self.train_dataset = datasets.ImageFolder(
                 train_dir,
-                transforms.Compose(
-                    [
-                        transforms.RandomResizedCrop(224),
-                        transforms.RandomHorizontalFlip(),
-                        transforms.ToTensor(),
-                        normalize,
-                    ]
-                ),
+                transforms.Compose([
+                    transforms.RandomResizedCrop(224),
+                    transforms.RandomHorizontalFlip(),
+                    transforms.ToTensor(),
+                    normalize,
+                ]),
             )
         # all stages will use the eval dataset
         normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

examples/pytorch/domain_templates/reinforce_learn_ppo.py

@@ -28,6 +28,7 @@
 [3] https://github.com/sid-sundrani/ppo_lightning
 
 """
+
 import argparse
 from typing import Callable, Iterator, List, Tuple
 

examples/pytorch/domain_templates/semantic_segmentation.py

@@ -333,14 +333,10 @@ def __init__(
         self.net = UNet(
             num_classes=19, num_layers=self.num_layers, features_start=self.features_start, bilinear=self.bilinear
         )
-        self.transform = transforms.Compose(
-            [
-                transforms.ToTensor(),
-                transforms.Normalize(
-                    mean=[0.35675976, 0.37380189, 0.3764753], std=[0.32064945, 0.32098866, 0.32325324]
-                ),
-            ]
-        )
+        self.transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.35675976, 0.37380189, 0.3764753], std=[0.32064945, 0.32098866, 0.32325324]),
+        ])
         self.trainset = KITTI(self.data_path, split="train", transform=self.transform)
         self.validset = KITTI(self.data_path, split="valid", transform=self.transform)
 

pyproject.toml

@@ -49,20 +49,20 @@ ignore-words-list = "te, compiletime"
 [tool.ruff]
 line-length = 120
 # Enable Pyflakes `E` and `F` codes by default.
-select = [
+lint.select = [
     "E", "W",  # see: https://pypi.org/project/pycodestyle
     "F",  # see: https://pypi.org/project/pyflakes
     "S",  # see: https://pypi.org/project/flake8-bandit
     "RUF018",  # see: https://docs.astral.sh/ruff/rules/assignment-in-assert
 ]
-extend-select = [
+lint.extend-select = [
     "I",  # see: isort
     "C4",  # see: https://pypi.org/project/flake8-comprehensions
     "SIM",  # see: https://pypi.org/project/flake8-simplify
     "RET",  # see: https://pypi.org/project/flake8-return
     "PT",  # see: https://pypi.org/project/flake8-pytest-style
 ]
-ignore = [
+lint.ignore = [
     "E731",  # Do not assign a lambda expression, use a def
     "S108",
     "E203", # conflicts with black
@@ -73,9 +73,9 @@ exclude = [
     "docs",
     "_notebooks"
 ]
-ignore-init-module-imports = true
+lint.ignore-init-module-imports = true
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 ".actions/*" = ["S101", "S310"]
 "setup.py" = ["S101"]
 "examples/**" = [
@@ -102,6 +102,9 @@ ignore-init-module-imports = true
     "RET504",  # todo:Unnecessary variable assignment before `return` statement
     "RET503",
 ]
+"src/lightning/data/**" = [
+    "S310",  # todo: Audit URL open for permitted schemes. Allowing use of `file:` or custom schemes is often unexpected.
+]
 "tests/**" = [
     "S101",  # Use of `assert` detected
     "S105", "S106",  # todo: Possible hardcoded password: ...
@@ -123,7 +126,7 @@ ignore-init-module-imports = true
     "PT019",  # todo: Fixture `_` without value is injected as parameter, use `@pytest.mark.usefixtures` instead
 ]
 
-[tool.ruff.mccabe]
+[tool.ruff.lint.mccabe]
 # Unlike Flake8, default to a complexity level of 10.
 max-complexity = 10