[misc] update pre-commit and run all files (#4752)

* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format

examples/images/diffusion/ldm/data/cifar10.py

@@ -1,15 +1,16 @@
-from typing import Dict
-import numpy as np
-from omegaconf import DictConfig, ListConfig
-import torch
-from torch.utils.data import Dataset
-from pathlib import Path
 import json
-from PIL import Image
-from torchvision import transforms
+from pathlib import Path
+from typing import Dict
+
+import torch
+from datasets import load_dataset
 from einops import rearrange
 from ldm.util import instantiate_from_config
-from datasets import load_dataset
+from omegaconf import DictConfig, ListConfig
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+
 
 def make_multi_folder_data(paths, caption_files=None, **kwargs):
     """Make a concat dataset from multiple folders
@@ -19,10 +20,9 @@ def make_multi_folder_data(paths, caption_files=None, **kwargs):
     """
     list_of_paths = []
     if isinstance(paths, (Dict, DictConfig)):
-        assert caption_files is None, \
-            "Caption files not yet supported for repeats"
+        assert caption_files is None, "Caption files not yet supported for repeats"
         for folder_path, repeats in paths.items():
-            list_of_paths.extend([folder_path]*repeats)
+            list_of_paths.extend([folder_path] * repeats)
         paths = list_of_paths
 
     if caption_files is not None:
@@ -31,8 +31,10 @@ def make_multi_folder_data(paths, caption_files=None, **kwargs):
         datasets = [FolderData(p, **kwargs) for p in paths]
     return torch.utils.data.ConcatDataset(datasets)
 
+
 class FolderData(Dataset):
-    def __init__(self,
+    def __init__(
+        self,
         root_dir,
         caption_file=None,
         image_transforms=[],
@@ -40,7 +42,7 @@ class FolderData(Dataset):
         default_caption="",
         postprocess=None,
         return_paths=False,
-        ) -> None:
+    ) -> None:
         """Create a dataset from a folder of images.
         If you pass in a root directory it will be searched for images
         ending in ext (ext can be a list)
@@ -75,12 +77,12 @@ class FolderData(Dataset):
             self.paths.extend(list(self.root_dir.rglob(f"*.{e}")))
         if isinstance(image_transforms, ListConfig):
             image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
-        image_transforms.extend([transforms.ToTensor(),
-                                 transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
+        image_transforms.extend(
+            [transforms.ToTensor(), transforms.Lambda(lambda x: rearrange(x * 2.0 - 1.0, "c h w -> h w c"))]
+        )
         image_transforms = transforms.Compose(image_transforms)
         self.tform = image_transforms
 
-
     def __len__(self):
         if self.captions is not None:
             return len(self.captions.keys())
@@ -94,7 +96,7 @@ class FolderData(Dataset):
             caption = self.captions.get(chosen, None)
             if caption is None:
                 caption = self.default_caption
-            filename = self.root_dir/chosen
+            filename = self.root_dir / chosen
         else:
             filename = self.paths[index]
 
@@ -119,22 +121,23 @@ class FolderData(Dataset):
         im = im.convert("RGB")
         return self.tform(im)
 
+
 def hf_dataset(
     name,
     image_transforms=[],
     image_column="img",
     label_column="label",
     text_column="txt",
-    split='train',
-    image_key='image',
-    caption_key='txt',
-    ):
-    """Make huggingface dataset with appropriate list of transforms applied
-    """
+    split="train",
+    image_key="image",
+    caption_key="txt",
+):
+    """Make huggingface dataset with appropriate list of transforms applied"""
     ds = load_dataset(name, split=split)
     image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
-    image_transforms.extend([transforms.ToTensor(),
-                                transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
+    image_transforms.extend(
+        [transforms.ToTensor(), transforms.Lambda(lambda x: rearrange(x * 2.0 - 1.0, "c h w -> h w c"))]
+    )
     tform = transforms.Compose(image_transforms)
 
     assert image_column in ds.column_names, f"Didn't find column {image_column} in {ds.column_names}"
@@ -144,7 +147,18 @@ def hf_dataset(
         processed = {}
         processed[image_key] = [tform(im) for im in examples[image_column]]
 
-        label_to_text_dict = {0: "airplane", 1: "automobile", 2: "bird", 3: "cat", 4: "deer", 5: "dog", 6: "frog", 7: "horse", 8: "ship", 9: "truck"}
+        label_to_text_dict = {
+            0: "airplane",
+            1: "automobile",
+            2: "bird",
+            3: "cat",
+            4: "deer",
+            5: "dog",
+            6: "frog",
+            7: "horse",
+            8: "ship",
+            9: "truck",
+        }
 
         processed[caption_key] = [label_to_text_dict[label] for label in examples[label_column]]
 
@@ -153,6 +167,7 @@ def hf_dataset(
     ds.set_transform(pre_process)
     return ds
 
+
 class TextOnly(Dataset):
     def __init__(self, captions, output_size, image_key="image", caption_key="txt", n_gpus=1):
         """Returns only captions with dummy images"""
@@ -166,7 +181,7 @@ class TextOnly(Dataset):
 
         if n_gpus > 1:
             # hack to make sure that all the captions appear on each gpu
-            repeated = [n_gpus*[x] for x in self.captions]
+            repeated = [n_gpus * [x] for x in self.captions]
             self.captions = []
             [self.captions.extend(x) for x in repeated]
 
@@ -175,10 +190,10 @@ class TextOnly(Dataset):
 
     def __getitem__(self, index):
         dummy_im = torch.zeros(3, self.output_size, self.output_size)
-        dummy_im = rearrange(dummy_im * 2. - 1., 'c h w -> h w c')
+        dummy_im = rearrange(dummy_im * 2.0 - 1.0, "c h w -> h w c")
         return {self.image_key: dummy_im, self.caption_key: self.captions[index]}
 
     def _load_caption_file(self, filename):
-        with open(filename, 'rt') as f:
+        with open(filename, "rt") as f:
             captions = f.readlines()
-        return [x.strip('\n') for x in captions]
+        return [x.strip("\n") for x in captions]