isort

.gitignore

@@ -2,3 +2,4 @@
 .vscode/
 pyrightconfig.json
 *.jpg
+*.pyc

.vscode/settings.json

@@ -0,0 +1,23 @@
+{
+    "files.insertFinalNewline": true,
+    "jupyter.debugJustMyCode": false,
+    "editor.formatOnSave": true,
+    "editor.formatOnPaste": true,
+    "files.autoSave": "onFocusChange",
+    "editor.defaultFormatter": "ms-python.black-formatter",
+    "black-formatter.path": ["/opt/homebrew/bin/black"],
+    "black-formatter.args": ["--config", "./pyproject.toml"],
+    "black-formatter.cwd": "${workspaceFolder}",
+    
+    "isort.check": true,
+    "python.analysis.typeCheckingMode": "basic",
+    "python.defaultInterpreterPath": "/opt/homebrew/bin/python3",
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnSave": true,
+        "editor.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        },
+    },
+    "isort.args":["--profile", "black"],
+}

pyproject.toml

@@ -0,0 +1,21 @@
+[tool.black]
+line_length = 100
+
+[tool.isort]
+honor_noqa = true
+line_length = 100
+profile = "black"
+verbose = false
+
+known_first_party = [
+	# all folders you want to lump
+  "src",
+]
+
+# Block below is google style import formatting https://pycqa.github.io/isort/docs/configuration/profiles.html
+force_sort_within_sections = true
+force_single_line = true
+lexicographical = true
+single_line_exclusions = ["typing"]
+order_by_type = false
+group_by_package = true

src/data.py

@@ -5,7 +5,8 @@ from typing import Any
 import datasets
 from PIL import Image
 import torch
-from torch.utils.data import Dataset, DataLoader
+from torch.utils.data import DataLoader
+from torch.utils.data import Dataset
 from torchvision import transforms
 
 from src import config
@@ -92,6 +93,7 @@ def get_dataset(
 if __name__ == "__main__":
     # do not want to do these imports in general
     import os
+
     from tqdm.auto import tqdm
 
     os.environ["TOKENIZERS_PARALLELISM"] = "false"

src/download.py

@@ -1,11 +1,11 @@
+from functools import partial
 from io import BytesIO
 import pathlib
-from functools import partial
 from typing import Any
 
 import datasets
-from PIL import Image
 from loguru import logger
+from PIL import Image
 import requests
 from tqdm.auto import tqdm
 

src/models.py

@@ -1,14 +1,15 @@
 from PIL import Image
-import transformers
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-
+import transformers
 from transformers import PreTrainedModel
 
-from src.config import TinyCLIPConfig, TinyCLIPTextConfig, TinyCLIPVisionConfig
 from src import loss
 from src import vision_model
+from src.config import TinyCLIPConfig
+from src.config import TinyCLIPTextConfig
+from src.config import TinyCLIPVisionConfig
 
 
 class Projection(nn.Module):