[pre-commit.ci] pre-commit autoupdate (#206)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.9 → v0.3.5](astral-sh/ruff-pre-commit@v0.1.9...v0.3.5)

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

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

---------

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

.pre-commit-config.yaml

@@ -11,7 +11,7 @@ repos:
     - id: end-of-file-fixer
     - id: trailing-whitespace
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.1.9
+  rev: v0.3.5
   hooks:
     - id: ruff  # Run the linter
       args: [ --fix ]

scripts/pipeline/datacube.py

@@ -26,6 +26,7 @@
       Process Sentinel-2, Sentinel-1, and DEM data for a specified time range,
       area of interest, and resolution.
 """
+
 import logging
 import os
 import random

scripts/pipeline/tile.py

@@ -8,6 +8,7 @@
 It includes functions to filter tiles based on cloud coverage and no-data pixels,
 and a tiling function that generates smaller tiles from the input stack.
 """
+
 import logging
 import subprocess
 import tempfile

src/callbacks_wandb.py

@@ -23,6 +23,7 @@
 - https://wandb.ai/wandb/wandb-lightning/reports/Image-Classification-using-PyTorch-Lightning--VmlldzoyODk1NzY
 - https://github.com/ashleve/lightning-hydra-template/blob/wandb-callbacks/src/callbacks/wandb_callbacks.py#L245
 """
+
 import lightning as L
 import matplotlib.pyplot as plt
 import numpy as np

src/datamodule.py

@@ -2,6 +2,7 @@
 LightningDataModule to load Earth Observation data from GeoTIFF files using
 rasterio.
 """
+
 import math
 import os
 import random

src/model_clay.py

@@ -481,12 +481,12 @@ def reconstruct_and_add_encoding(
         decoder_patches = torch.zeros(
             (B, self.num_patches, self.dim), device=unmasked_patches.device
         )  # [B GL D]
-        decoder_patches[
-            batch_indices, unmasked_indices, :
-        ] = unmasked_patches  # [B GL:(1 - mask_ratio) D]
-        decoder_patches[
-            batch_indices, masked_indices, :
-        ] = masked_patches  # [B GL:mask_ratio D]
+        decoder_patches[batch_indices, unmasked_indices, :] = (
+            unmasked_patches  # [B GL:(1 - mask_ratio) D]
+        )
+        decoder_patches[batch_indices, masked_indices, :] = (
+            masked_patches  # [B GL:mask_ratio D]
+        )
 
         return decoder_patches  # [B GL D]
 

src/model_vit.py

@@ -4,6 +4,7 @@
 Code structure adapted from Lightning project seed at
 https://github.com/Lightning-AI/deep-learning-project-template
 """
+
 import os
 import re
 

src/tests/test_callbacks.py

@@ -4,6 +4,7 @@
 Checks to ensure that the hooks in callbacks can be triggered and produce the
 correct output results.
 """
+
 import tempfile
 
 import lightning as L

src/tests/test_datamodule.py

@@ -4,6 +4,7 @@
 Integration test for the entire data pipeline from loading the data and
 pre-processing steps, up to the DataLoader producing mini-batches.
 """
+
 import tempfile
 
 import lightning as L

src/tests/test_model.py

@@ -4,6 +4,7 @@
 Based loosely on Lightning's testing method described at
 https://github.com/Lightning-AI/lightning/blob/2.1.0/.github/CONTRIBUTING.md#how-to-add-new-tests
 """
+
 import os
 import tempfile
 

src/tests/test_trainer.py

@@ -4,6 +4,7 @@
 Based on advanced usage of running LightningCLI from Python at
 https://lightning.ai/docs/pytorch/2.1.0/cli/lightning_cli_advanced_3.html#run-from-python
 """
+
 import pytest
 
 from trainer import cli_main

src/utils.py

@@ -1,6 +1,7 @@
 """
 Code from https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/simple_vit.py
 """
+
 import torch