Update quality tooling for formatting (#21480)

* Result of black 23.1

* Update target to Python 3.7

* Switch flake8 to ruff

* Configure isort

* Configure isort

* Apply isort with line limit

* Put the right black version

* adapt black in check copies

* Fix copies

examples/research_projects/visual_bert/extracting_data.py

@@ -9,9 +9,9 @@ from collections import OrderedDict
 import datasets
 import numpy as np
 import torch
-
 from modeling_frcnn import GeneralizedRCNN
 from processing_image import Preprocess
+
 from utils import Config
 
 

examples/research_projects/visual_bert/modeling_frcnn.py

@@ -169,7 +169,6 @@ def get_norm(norm, out_channels):
 
 
 def _create_grid_offsets(size: List[int], stride: int, offset: float, device):
-
     grid_height, grid_width = size
     shifts_x = torch.arange(
         offset * stride,
@@ -390,7 +389,6 @@ def assign_boxes_to_levels(
     canonical_box_size: int,
     canonical_level: int,
 ):
-
     box_sizes = torch.sqrt(torch.cat([boxes.area() for boxes in box_lists]))
     # Eqn.(1) in FPN paper
     level_assignments = torch.floor(canonical_level + torch.log2(box_sizes / canonical_box_size + 1e-8))
@@ -1708,9 +1706,10 @@ class GeneralizedRCNN(nn.Module):
             elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
                 archive_file = pretrained_model_name_or_path
             elif os.path.isfile(pretrained_model_name_or_path + ".index"):
-                assert from_tf, (
-                    "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint"
-                    .format(pretrained_model_name_or_path + ".index")
+                assert (
+                    from_tf
+                ), "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format(
+                    pretrained_model_name_or_path + ".index"
                 )
                 archive_file = pretrained_model_name_or_path + ".index"
             else:

examples/research_projects/visual_bert/utils.py

@@ -34,14 +34,13 @@ from pathlib import Path
 from urllib.parse import urlparse
 from zipfile import ZipFile, is_zipfile
 
-import numpy as np
-from PIL import Image
-from tqdm.auto import tqdm
-
 import cv2
+import numpy as np
 import requests
 import wget
 from filelock import FileLock
+from PIL import Image
+from tqdm.auto import tqdm
 from yaml import Loader, dump, load
 
 
@@ -181,7 +180,6 @@ class Config:
 
     @classmethod
     def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs):
-
         cache_dir = kwargs.pop("cache_dir", None)
         force_download = kwargs.pop("force_download", False)
         resume_download = kwargs.pop("resume_download", False)
@@ -225,14 +223,13 @@ class Config:
 
 # quick compare tensors
 def compare(in_tensor):
-
     out_tensor = torch.load("dump.pt", map_location=in_tensor.device)
     n1 = in_tensor.numpy()
     n2 = out_tensor.numpy()[0]
     print(n1.shape, n1[0, 0, :5])
     print(n2.shape, n2[0, 0, :5])
     assert np.allclose(n1, n2, rtol=0.01, atol=0.1), (
-        f"{sum([1 for x in np.isclose(n1, n2, rtol=0.01, atol=0.1).flatten() if x == False])/len(n1.flatten())*100:.4f} %"
+        f"{sum([1 for x in np.isclose(n1, n2, rtol=0.01, atol=0.1).flatten() if x is False])/len(n1.flatten())*100:.4f} %"
         " element-wise mismatch"
     )
     raise Exception("tensors are all good")
@@ -300,7 +297,6 @@ def get_from_cache(
     user_agent=None,
     local_files_only=False,
 ):
-
     if cache_dir is None:
         cache_dir = TRANSFORMERS_CACHE
     if isinstance(cache_dir, Path):
@@ -355,7 +351,6 @@ def get_from_cache(
     # Prevent parallel downloads of the same file with a lock.
     lock_path = cache_path + ".lock"
     with FileLock(lock_path):
-
         # If the download just completed while the lock was activated.
         if os.path.exists(cache_path) and not force_download:
             # Even if returning early like here, the lock will be released.
@@ -406,7 +401,6 @@ def get_from_cache(
 
 
 def url_to_filename(url, etag=None):
-
     url_bytes = url.encode("utf-8")
     url_hash = sha256(url_bytes)
     filename = url_hash.hexdigest()

examples/research_projects/visual_bert/visualizing_image.py

@@ -18,6 +18,7 @@
 import colorsys
 import io
 
+import cv2
 import matplotlib as mpl
 import matplotlib.colors as mplc
 import matplotlib.figure as mplfigure
@@ -25,7 +26,6 @@ import numpy as np
 import torch
 from matplotlib.backends.backend_agg import FigureCanvasAgg
 
-import cv2
 from utils import img_tensorize