fixing

.actions/assistant.py

@@ -514,7 +514,7 @@ def group_folders(
         # unique folders
         dirs = set(dirs)
         # drop folder with skip folder
-        dirs = [pd for pd in dirs if not any(nd in AssistantCLI._SKIP_DIRS for nd in pd.split(os.path.sep))]
+        dirs = [pdir for pdir in dirs if not any(ndir in AssistantCLI._SKIP_DIRS for ndir in pdir.split(os.path.sep))]
         # valid folder has meta
         dirs_exist = [d for d in dirs if os.path.isdir(d)]
         dirs_invalid = [d for d in dirs_exist if not AssistantCLI._find_meta(d)]

.pre-commit-config.yaml

@@ -34,7 +34,7 @@ repos:
     hooks:
       - id: codespell
         additional_dependencies: [tomli]
-        args: ["--write-changes"]
+        #args: ["--write-changes"]
 
   - repo: https://github.com/PyCQA/docformatter
     rev: v1.7.3
@@ -45,6 +45,13 @@ repos:
           - "--wrap-summaries=119"
           - "--wrap-descriptions=120"
 
+  - repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v3.0.3
+    hooks:
+      - id: prettier
+        # https://prettier.io/docs/en/options.html#print-width
+        args: ["--print-width=120"]
+
   - repo: https://github.com/psf/black
     rev: 23.3.0
     hooks:

course_UvA-DL/09-normalizing-flows/NF_image_modeling.py

@@ -359,7 +359,7 @@ def test_step(self, batch, batch_idx):
 # ### Dequantization
 #
 # Normalizing flows rely on the rule of change of variables, which is naturally defined in continuous space.
-# Applying flows directly on discrete data leads to undesired density models where arbitrarly high likelihood are placed on a few, particular values.
+# Applying flows directly on discrete data leads to undesired density models where arbitrarily high likelihood are placed on a few, particular values.
 # See the illustration below:
 #
 # <center><img src="dequantization_issue.svg" width="40%"/></center>

lightning_examples/barlow-twins/barlow_twins.py

@@ -206,7 +206,7 @@ def forward(self, z1, z2):
 # %% [markdown]
 # ### Projection head
 #
-# Unlike SimCLR and BYOL, the downstream performance of Barlow Twins greatly benefits from having a larger projection head after the backbone network. The paper utilizes a 3 layer MLP with 8192 hidden dimensions and 8192 as the output dimenion of the projection head. For the purposes of the tutorial, we use a smaller projection head. But, it is imperative to mention here that in practice, Barlow Twins needs to be trained using a bigger projection head as it is highly sensitive to its architecture and output dimensionality.
+# Unlike SimCLR and BYOL, the downstream performance of Barlow Twins greatly benefits from having a larger projection head after the backbone network. The paper utilizes a 3 layer MLP with 8192 hidden dimensions and 8192 as the output dimension of the projection head. For the purposes of the tutorial, we use a smaller projection head. But, it is imperative to mention here that in practice, Barlow Twins needs to be trained using a bigger projection head as it is highly sensitive to its architecture and output dimensionality.
 
 
 # %%

pyproject.toml

@@ -50,7 +50,7 @@ quiet-level = 3
 #  https://github.com/codespell-project/codespell/issues/2839#issuecomment-1731601603
 # also adding links until they ignored by its: nature
 #  https://github.com/codespell-project/codespell/issues/2243#issuecomment-1732019960
-#ignore-words-list = ""
+ignore-words-list = "nd"
 
 
 [tool.ruff]