chore: import upstream snapshot with attribution

doc/source/train/doc_code/random_text_generator.py

@@ -0,0 +1,58 @@
+import random
+import string
+import ray
+
+def random_text(length: int) -> str:
+    """Generate random text of specified length."""
+    if length <= 0:
+        return ""
+
+    if length <= 3:
+        return "".join(random.choices(string.ascii_lowercase, k=length))
+
+    words = []
+    current_length = 0
+
+    while current_length < length:
+        remaining = length - current_length
+        
+        if remaining <= 4:
+            word_length = remaining
+            word = "".join(random.choices(string.ascii_lowercase, k=word_length))
+            words.append(word)
+            break
+        else:
+            max_word_length = min(10, remaining - 1)
+            if max_word_length >= 3:
+                word_length = random.randint(3, max_word_length)
+            else:
+                word_length = remaining
+            word = "".join(random.choices(string.ascii_lowercase, k=word_length))
+            words.append(word)
+            current_length += len(word) + 1
+
+    text = " ".join(words)
+    return text[:length]
+
+def random_label() -> int:
+    """Pick a random label."""
+    labels = [0, 1, 2, 3, 4, 5, 6, 7]
+    return random.choice(labels)
+
+def create_mock_ray_text_dataset(dataset_size: int = 96, min_len: int = 5, max_len: int = 100):
+    """Create a mock Ray dataset with random text and labels."""
+    numbers = random.choices(range(min_len, max_len + 1), k=dataset_size)
+    ray_dataset = ray.data.from_items(numbers)
+
+    def map_to_text_and_label(item):
+        length = item['item']
+        text = random_text(length)
+        label = random_label()
+        return {
+            "length": length,
+            "text": text,
+            "label": label
+        }
+
+    text_dataset = ray_dataset.map(map_to_text_and_label)
+    return text_dataset