precommit: yapf (#5494)

* precommit: yapf

* align isort

* fix

# Conflicts:
#	utils/plots.py

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* Update setup.cfg

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* Update setup.cfg

* Update setup.cfg

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* Update wandb_utils.py

* Update augmentations.py

* Update setup.cfg

* Update yolo.py

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* Update val.py

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* simplify colorstr

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* val run fix

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* PyTorch Hub tuple fix

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

models/common.py

@@ -227,11 +227,12 @@ class GhostBottleneck(nn.Module):
     def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
         super().__init__()
         c_ = c2 // 2
-        self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1),  # pw
-                                  DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
-                                  GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
-        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
-                                      Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
+        self.conv = nn.Sequential(
+            GhostConv(c1, c_, 1, 1),  # pw
+            DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
+            GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
+        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
+                                                                            act=False)) if s == 2 else nn.Identity()
 
     def forward(self, x):
         return self.conv(x) + self.shortcut(x)
@@ -387,9 +388,10 @@ class DetectMultiBackend(nn.Module):
                     Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
                 if edgetpu:  # Edge TPU https://coral.ai/software/#edgetpu-runtime
                     LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
-                    delegate = {'Linux': 'libedgetpu.so.1',
-                                'Darwin': 'libedgetpu.1.dylib',
-                                'Windows': 'edgetpu.dll'}[platform.system()]
+                    delegate = {
+                        'Linux': 'libedgetpu.so.1',
+                        'Darwin': 'libedgetpu.1.dylib',
+                        'Windows': 'edgetpu.dll'}[platform.system()]
                     interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
                 else:  # Lite
                     LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
@@ -531,7 +533,7 @@ class AutoShape(nn.Module):
                 return self.model(imgs.to(p.device).type_as(p), augment, profile)  # inference
 
         # Pre-process
-        n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs])  # number of images, list of images
+        n, imgs = (len(imgs), list(imgs)) if isinstance(imgs, (list, tuple)) else (1, [imgs])  # number, list of images
         shape0, shape1, files = [], [], []  # image and inference shapes, filenames
         for i, im in enumerate(imgs):
             f = f'image{i}'  # filename
@@ -561,8 +563,13 @@ class AutoShape(nn.Module):
             t.append(time_sync())
 
             # Post-process
-            y = non_max_suppression(y if self.dmb else y[0], self.conf, self.iou, self.classes, self.agnostic,
-                                    self.multi_label, max_det=self.max_det)  # NMS
+            y = non_max_suppression(y if self.dmb else y[0],
+                                    self.conf,
+                                    self.iou,
+                                    self.classes,
+                                    self.agnostic,
+                                    self.multi_label,
+                                    max_det=self.max_det)  # NMS
             for i in range(n):
                 scale_coords(shape1, y[i][:, :4], shape0[i])
 
@@ -603,8 +610,12 @@ class Detections:
                         label = f'{self.names[int(cls)]} {conf:.2f}'
                         if crop:
                             file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
-                            crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label,
-                                          'im': save_one_box(box, im, file=file, save=save)})
+                            crops.append({
+                                'box': box,
+                                'conf': conf,
+                                'cls': cls,
+                                'label': label,
+                                'im': save_one_box(box, im, file=file, save=save)})
                         else:  # all others
                             annotator.box_label(box, label if labels else '', color=colors(cls))
                     im = annotator.im