[pre-commit.ci] pre-commit suggestions

.actions/assistant.py

@@ -188,7 +188,7 @@ def _load_meta(folder: str, strict: bool = False) -> Optional[dict]:
 
         Args:
             folder: path to the folder with python script, meta and artefacts
-            strict: raise error if meta is missing required feilds
+            strict: raise error if meta is missing required fields
         """
         fpath = AssistantCLI._find_meta(folder)
         assert fpath, f"Missing meta file in folder: {folder}"

.pre-commit-config.yaml

@@ -23,21 +23,21 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.8.0
+    rev: v3.14.0
     hooks:
       - id: pyupgrade
         args: ["--py38-plus"]
         name: Upgrade code
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.5
+    rev: v2.2.6
     hooks:
       - id: codespell
         additional_dependencies: [tomli]
         #args: ["--write-changes"]
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.7.3
+    rev: v1.7.5
     hooks:
       - id: docformatter
         args:
@@ -53,7 +53,7 @@ repos:
         args: ["--print-width=120"]
 
   - repo: https://github.com/psf/black
-    rev: 23.3.0
+    rev: 23.9.1
     hooks:
       - id: black
         name: Format code
@@ -64,7 +64,7 @@ repos:
       - id: yesqa
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.16
+    rev: 0.7.17
     hooks:
       - id: mdformat
         additional_dependencies:
@@ -73,7 +73,7 @@ repos:
           - mdformat_frontmatter
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.276
+    rev: v0.0.292
     hooks:
       - id: ruff
         args: ["--fix"]

course_UvA-DL/06-graph-neural-networks/GNN_overview.py

@@ -843,7 +843,7 @@ def print_results(result_dict):
 # In this case, we will use the average pooling.
 # Hence, we need to know which nodes should be included in which average pool.
 # Using this pooling, we can already create our graph network below.
-# Specifically, we re-use our class `GNNModel` from before,
+# Specifically, we reuse our class `GNNModel` from before,
 # and simply add an average pool and single linear layer for the graph prediction task.
 
 

course_UvA-DL/07-deep-energy-based-generative-models/Deep_Energy_Models.py

@@ -315,7 +315,7 @@ def forward(self, x):
 # inside the MCMC sampling to obtain reasonable samples.
 # However, there is a training trick that significantly reduces the sampling cost: using a sampling buffer.
 # The idea is that we store the samples of the last couple of batches in a buffer,
-# and re-use those as the starting point of the MCMC algorithm for the next batches.
+# and reuse those as the starting point of the MCMC algorithm for the next batches.
 # This reduces the sampling cost because the model requires a significantly
 # lower number of steps to converge to reasonable samples.
 # However, to not solely rely on previous samples and allow novel samples as well,

course_UvA-DL/10-autoregressive-image-modeling/Autoregressive_Image_Modeling.py

@@ -905,7 +905,7 @@ def autocomplete_image(img):
 # potentially undesirable behavior. For instance, the value 242 has a
 # 1000x lower likelihood than 243 although they are extremely close and
 # can often not be distinguished. This shows that the model might have not
-# generlized well over pixel values. The better solution to this problem
+# generalized well over pixel values. The better solution to this problem
 # is to use discrete logitics mixtures instead of a softmax distribution.
 # A discrete logistic distribution can be imagined as discretized, binned
 # Gaussians. Using a mixture of discrete logistics instead of a softmax

flash_tutorials/text_classification/text_classification.py

@@ -5,7 +5,7 @@
 #
 # Finetuning consists of four steps:
 #
-#  - 1. Train a source neural network model on a source dataset. For text classication, it is traditionally  a transformer model such as BERT [Bidirectional Encoder Representations from Transformers](https://arxiv.org/abs/1810.04805) trained on wikipedia.
+#  - 1. Train a source neural network model on a source dataset. For text classification, it is traditionally  a transformer model such as BERT [Bidirectional Encoder Representations from Transformers](https://arxiv.org/abs/1810.04805) trained on wikipedia.
 # As those model are costly to train, [Transformers](https://github.com/huggingface/transformers) or [FairSeq](https://github.com/pytorch/fairseq) libraries provides popular pre-trained model architectures for NLP. In this notebook, we will be using [tiny-bert](https://huggingface.co/prajjwal1/bert-tiny).
 #
 #  - 2. Create a new neural network the target model. Its architecture replicates all model designs and their parameters on the source model, expect the latest layer which is removed. This model without its latest layers is traditionally called a backbone

lightning_examples/finetuning-scheduler/finetuning-scheduler.py

@@ -235,8 +235,7 @@ def __init__(
         tokenizers_parallelism: bool = True,
         **dataloader_kwargs: Any,
     ):
-        r"""Initialize the ``LightningDataModule`` designed for both the RTE or BoolQ SuperGLUE Hugging Face
-        datasets.
+        r"""Initialize the ``LightningDataModule`` designed for both the RTE or BoolQ SuperGLUE Hugging Face datasets.
 
         Args:
             model_name_or_path (str):