fix: pre-commit formatting

Author: baptiste-olivier

docs/sdk/tutorials/finetuning_dinov2.md

@@ -188,7 +188,7 @@ We can then prepare the assets to be uploaded to Kili:
 # sort the assets by alternating between classes so that both
 # classes show up in the first page of the labeling interface
 content_array = []
-iterator = zip((data_dir / "def_front").iterdir(), (data_dir / "ok_front").iterdir())
+iterator = zip((data_dir / "def_front").iterdir(), (data_dir / "ok_front").iterdir(), strict=False)
 for filepath_def, filepath_ok in iterator:
     content_array.append(filepath_def)
     content_array.append(filepath_ok)

docs/sdk/tutorials/importing_coco.md

@@ -548,17 +548,15 @@ Below, we import some useful functions to convert annotations to Kili label form
 
 
 ```python
-from typing import Dict, List
-
 from kili.utils.labels.bbox import bbox_points_to_normalized_vertices, point_to_normalized_point
 from kili.utils.labels.image import mask_to_normalized_vertices
 ```
 
 
 ```python
 def coco_bbox_annotation_to_normalized_vertices(
-    coco_ann: Dict, *, img_width: int, img_height: int
-) -> List[Dict]:
+    coco_ann: dict, *, img_width: int, img_height: int
+) -> list[dict]:
     x, y, width, height = coco_ann["bbox"]
     ret = bbox_points_to_normalized_vertices(
         bottom_left={"x": x, "y": y + height},
@@ -575,8 +573,8 @@ def coco_bbox_annotation_to_normalized_vertices(
 
 ```python
 def coco_segm_annotation_to_normalized_vertices(
-    coco_ann: Dict, *, img_width: int, img_height: int
-) -> List[List[Dict]]:
+    coco_ann: dict, *, img_width: int, img_height: int
+) -> list[list[dict]]:
     coco_segmentations = coco_ann["segmentation"]
 
     ret = []
@@ -590,7 +588,7 @@ def coco_segm_annotation_to_normalized_vertices(
                     img_width=img_width,
                     origin_location="top_left",
                 )
-                for x, y in zip(coco_segm[::2], coco_segm[1::2])
+                for x, y in zip(coco_segm[::2], coco_segm[1::2], strict=False)
             ]
             ret.append(vertices)
 
@@ -706,6 +704,7 @@ for image_id in external_id_array:
                 keypoints[1::3],
                 keypoints[2::3],
                 person_keypoints_val2017["categories"][0]["keypoints"],
+                strict=False,
             ):
                 if x == y == visibility == 0:
                     continue

docs/sdk/tutorials/importing_pascalvoc.md

@@ -61,7 +61,9 @@ Path(images_folder).mkdir(parents=True, exist_ok=True)
 
 ```python
 for image_name in images_names:
-    for fld, img_ext in zip([images_folder, annos_folder], [images_extension, ".xml"]):
+    for fld, img_ext in zip(
+        [images_folder, annos_folder], [images_extension, ".xml"], strict=False
+    ):
         url = f"https://raw.githubusercontent.com/kili-technology/kili-python-sdk/main/recipes/datasets/pascalVOC2012/valsubset/{fld}/{image_name}{img_ext}"
         urllib.request.urlretrieve(url, f"{fld}/{image_name}{img_ext}")
 ```
@@ -190,16 +192,14 @@ Below, we import an useful function to convert annotations to Kili label format.
 
 
 ```python
-from typing import Dict, List
-
 from kili.utils.labels.bbox import bbox_points_to_normalized_vertices
 ```
 
 
 ```python
 def pascal_bbox_to_kili_normalized_vertices(
-    pascal_bbox: Dict, img_width: int, img_height: int
-) -> List[Dict]:
+    pascal_bbox: dict, img_width: int, img_height: int
+) -> list[dict]:
     x1, y1, x2, y2 = (
         pascal_bbox["xmin"],
         pascal_bbox["ymin"],

docs/sdk/tutorials/medical_imaging.md

@@ -244,7 +244,7 @@ colors = [
 ]
 CLASS_TO_COLOR = {}
 for class_name, color in zip(
-    json_interface["jobs"]["JOB_0"]["content"]["categories"].keys(), colors
+    json_interface["jobs"]["JOB_0"]["content"]["categories"].keys(), colors, strict=False
 ):
     CLASS_TO_COLOR[class_name] = color
 print(CLASS_TO_COLOR)

docs/sdk/tutorials/ner_pre_annotations_openai.md

@@ -315,7 +315,8 @@ ENTITY_TYPES = [
 ]
 
 ENTITY_TYPES_WITH_COLORS = [
-    (entity_type[0], entity_type[1], color) for entity_type, color in zip(ENTITY_TYPES, COLORS)
+    (entity_type[0], entity_type[1], color)
+    for entity_type, color in zip(ENTITY_TYPES, COLORS, strict=False)
 ]
 print(ENTITY_TYPES_WITH_COLORS)
 ```
@@ -405,7 +406,7 @@ We can finally import our OpenAI-generated pre-annotations!
 ```python
 json_response_array = []
 
-for datapoint, sentence_annotations in zip(dataset, openai_answers):
+for datapoint, sentence_annotations in zip(dataset, openai_answers, strict=False):
     full_sentence = datapoint["sentence"]
     annotations = []  # list of annotations for the sentence
     for category, _ in ENTITY_TYPES:
@@ -490,7 +491,7 @@ def format_sentence_annotations(sentence_annotations):
 
 references = []
 predictions = []
-for datapoint, sentence_annotations in zip(dataset, openai_answers):
+for datapoint, sentence_annotations in zip(dataset, openai_answers, strict=False):
     references.append(datapoint["ner_tags"])
 
     sentence_annotations = format_sentence_annotations(sentence_annotations)

docs/sdk/tutorials/plugins_development.md

@@ -86,14 +86,12 @@ We recommend using a modern IDE like VScode to get type hints and autocompletion
 
 
 ```python
-from typing import Dict
-
 import numpy as np
 
 from kili.plugins import PluginCore
 
 
-def custom_function(label: Dict):
+def custom_function(label: dict):
     label_id = label.get("id")
     print(f"My custom function for review of label with id {label_id}")
 
@@ -113,11 +111,11 @@ class PluginHandler(PluginCore):
                 text_array=["Random issue generated for this label"],
             )
 
-    def on_review(self, label: Dict, asset_id: str) -> None:
+    def on_review(self, label: dict, asset_id: str) -> None:
         """Dedicated handler for Review action"""
         custom_function(label)
 
-    def on_submit(self, label: Dict, asset_id: str) -> None:
+    def on_submit(self, label: dict, asset_id: str) -> None:
         """Dedicated handler for Submit action"""
         print("On submit called")
 

docs/sdk/tutorials/vertex_ai_automl_od.md

@@ -35,7 +35,7 @@ import json
 import mimetypes
 import random
 from pathlib import Path
-from typing import List, Union
+from typing import Union
 
 import requests
 from google.cloud import aiplatform, storage
@@ -139,7 +139,7 @@ When importing data to a Vertex AI Datset, the images must already be stored on
 
 
 ```python
-def upload_assets_to_bucket(assets: List[dict], bucket_name: str, bucket_dataset_dir: str):
+def upload_assets_to_bucket(assets: list[dict], bucket_name: str, bucket_dataset_dir: str):
     bucket = storage_client.get_bucket(bucket_name)
     for asset in tqdm(assets, desc="uploading assets to bucket"):
         image_bucket_path = f"{bucket_dataset_dir}/images/{Path(asset['content']).name}"
@@ -230,7 +230,7 @@ This jsonl file then needs to be imported to the Google Cloud Storage bucket and
 
 ```python
 def generate_and_upload_inputs_to_bucket(
-    assets: List[dict], bucket_name: str, bucket_dataset_dir: str
+    assets: list[dict], bucket_name: str, bucket_dataset_dir: str
 ):
     output_jsonl_file = "inputs.jsonl"
     bucket = storage_client.get_bucket(bucket_name)
@@ -273,7 +273,7 @@ def create_and_import_dataset_image_sample(
     project: str,
     location: str,
     display_name: str,
-    src_uris: Union[str, List[str]],
+    src_uris: Union[str, list[str]],
     import_schema_uri: str,
     sync: bool = True,
 ):
@@ -470,7 +470,7 @@ upload_assets_to_bucket(unlabeled_assets, bucket_name, bucket_dataset_dir)
 
 
 ```python
-def upload_test_source_to_bucket(assets: List[dict], bucket_name: str, bucket_dataset_dir: str):
+def upload_test_source_to_bucket(assets: list[dict], bucket_name: str, bucket_dataset_dir: str):
     output_jsonl_file = "batch_inference_inputs.jsonl"
     bucket = storage_client.get_bucket(bucket_name)
     with open(output_jsonl_file, "w") as output_file:
@@ -696,6 +696,7 @@ def vertex_to_kili(json_output):
         json_output["prediction"]["displayNames"],
         json_output["prediction"]["bboxes"],
         json_output["prediction"]["confidence"],
+        strict=False,
     ):
         bounding_poly = [
             {"x": bbox[0], "y": bbox[3]},

docs/utils.py

@@ -14,11 +14,12 @@
 import re
 import shutil
 from binascii import a2b_base64
+from collections.abc import Sequence
 from io import BytesIO
 from itertools import groupby
 from pathlib import Path
 from tempfile import NamedTemporaryFile
-from typing import Dict, Optional, Sequence
+from typing import Optional
 
 import click
 import urllib3
@@ -95,7 +96,7 @@ def preprocess_cell(self, cell, resources, index):
         return cell, resources
 
 
-def embed_images_in_markdown(markdown: str, images: Dict[str, bytes], notebook_dir: Path) -> str:
+def embed_images_in_markdown(markdown: str, images: dict[str, bytes], notebook_dir: Path) -> str:
     """Embed images in markdown in base64."""
     md_img_pattern = r"!\[(.*?)\]\((.*?)\)"  # matches ![]()
     matched_images = re.findall(md_img_pattern, markdown)

recipes/counterfactual_data_augmentation.ipynb

@@ -214,6 +214,7 @@
     "    for review_type, project_imdb in zip(\n",
     "        [\"Positive\", \"Negative\"],\n",
     "        [project_imdb_positive_to_negative, project_imdb_negative_to_positive],\n",
+    "        strict=False,\n",
     "    ):\n",
     "        dataframe = df[df[\"Sentiment\"] == review_type]\n",
     "        reviews_to_import = dataframe[\"Text\"].tolist()\n",
@@ -253,6 +254,7 @@
     "    for review_type, project_imdb in zip(\n",
     "        [\"Positive\", \"Negative\"],\n",
     "        [project_imdb_positive_to_negative, project_imdb_negative_to_positive],\n",
+    "        strict=False,\n",
     "    ):\n",
     "        dataframe = df[df[\"Sentiment\"] != review_type]\n",
     "\n",

recipes/finetuning_dinov2.ipynb

@@ -354,7 +354,7 @@
     "# sort the assets by alternating between classes so that both\n",
     "# classes show up in the first page of the labeling interface\n",
     "content_array = []\n",
-    "iterator = zip((data_dir / \"def_front\").iterdir(), (data_dir / \"ok_front\").iterdir())\n",
+    "iterator = zip((data_dir / \"def_front\").iterdir(), (data_dir / \"ok_front\").iterdir(), strict=False)\n",
     "for filepath_def, filepath_ok in iterator:\n",
     "    content_array.append(filepath_def)\n",
     "    content_array.append(filepath_ok)\n",