chore: import upstream snapshot with attribution

tools/mtmd/debug/mtmd-debug.md

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+# mtmd-debug
+
+## Debugging encode pass
+
+Example of debugging an input gray image (raw, not preprocessed):
+
+```py
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained(...)
+
+def test_vision():
+  img_size = 896 # number of patches per side
+  pixel_values = torch.zeros(1, 3, img_size, img_size) + 0.5 # gray image
+  with torch.no_grad():
+    outputs = model.model.get_image_features(pixel_values=pixel_values)
+  print("last_hidden_state shape:", outputs.last_hidden_state.shape)
+  print("last_hidden_state:", outputs.last_hidden_state)
+
+test_vision()
+```
+
+Example of debugging a rainbow image:
+
+```py
+import torch
+import math
+
+def make_rainbow(img_size):
+    cx, cy = img_size / 2.0, img_size / 2.0
+    max_dist = math.sqrt(cx * cx + cy * cy)
+    img = torch.zeros(1, 3, img_size, img_size)
+    for y in range(img_size):
+        for x in range(img_size):
+            dx, dy = x - cx, y - cy
+            hue = math.atan2(dy, dx) / (2 * math.pi)
+            if hue < 0:
+                hue += 1
+            sat = math.sqrt(dx * dx + dy * dy) / max_dist
+            sat = min(sat, 1.0)
+            h6 = hue * 6
+            i6 = int(h6)
+            f = h6 - i6
+            p = 1 - sat
+            q = 1 - sat * f
+            t = 1 - sat * (1 - f)
+            rgb = [(1,t,p),(q,1,p),(p,1,t),(p,q,1),(t,p,1),(1,p,q)][i6 % 6]
+            img[0, 0, y, x] = rgb[0]
+            img[0, 1, y, x] = rgb[1]
+            img[0, 2, y, x] = rgb[2]
+    return img
+
+img_size = 896
+pixel_values = make_rainbow(img_size)
+with torch.no_grad():
+    outputs = model.model.get_image_features(pixel_values=pixel_values)
+print("last_hidden_state:", outputs.last_hidden_state)
+```
+
+## Debugging preprocess pass
+
+(TODO)