Create python generator files for gsm symbolic templates

reasoning_gym/arithmetic/gsm_symbolic/generator_0.py

@@ -0,0 +1,68 @@
+from random import Random
+from typing import Dict, Any
+import math
+
+def generate_from_variables(name: str, food: str, peel_rate: int, batch_size: int, 
+                          time_per_batch: int, total_amount: int) -> Dict[str, Any]:
+    
+    peel_time = total_amount // peel_rate
+    num_batches = total_amount // batch_size  
+    cook_time = num_batches * time_per_batch
+    total_time = peel_time + cook_time
+
+    question = f"{name} can peel {peel_rate} {food}s a minute and saute {batch_size} {food}s in {time_per_batch} minutes. How long will it take her to peel and saute {total_amount} {food}s?"
+    
+    answer_cot = f"First find how long it takes {name} to peel the {food}: {total_amount} {food} / {peel_rate} {food}/minute = {peel_time} minutes\n" \
+                 f"Then find how many batches of {food} she needs to cook: {total_amount} {food} / {batch_size} {food}/batch = {num_batches} batches\n" \
+                 f"Then multiply the number of batches by the time per batch to find the total cook time: {num_batches} batches * {time_per_batch} minutes/batch = {cook_time} minutes\n" \
+                 f"Then add the peeling time to find the total time {name} spends: {cook_time} minutes + {peel_time} minutes = {total_time} minutes\n" \
+                 f"#### {total_time}"
+
+    return {
+        'question': question,
+        'answer': str(total_time),
+        'answer_cot': answer_cot,
+        'answer_value': total_time,
+        'variables': {
+            'name': name,
+            'food': food,
+            'peel_rate': peel_rate,
+            'batch_size': batch_size,
+            'time_per_batch': time_per_batch, 
+            'total_amount': total_amount,
+            'peel_time': peel_time,
+            'cook_time': cook_time
+        }
+    }
+
+def generate_example(rng: Random, difficulty: float = 1.0) -> Dict[str, Any]:
+    names_female = ["Emily", "Sarah", "Emma", "Sophia", "Olivia", "Ava", "Isabella", "Mia"]
+    foods = ["shrimp", "onion", "carrot", "mushroom", "clam"]
+    
+    name = rng.choice(names_female)
+    food = rng.choice(foods)
+    
+    peel_rate = int(rng.randint(4, int(15 * difficulty)))
+    batch_size = int(rng.randrange(20, int(50 * difficulty), 5))
+    time_per_batch = int(rng.randint(5, int(20 * difficulty)))
+    
+    # Ensure total is divisible by both peel_rate and batch_size
+    lcm = peel_rate * batch_size // math.gcd(peel_rate, batch_size)
+    num_lcm = rng.randint(1, int(4 * difficulty))
+    total_amount = lcm * num_lcm
+    
+    result = generate_from_variables(name, food, peel_rate, batch_size, time_per_batch, total_amount)
+    
+    return {
+        'question': result['question'],
+        'answer': result['answer'],
+        'metadata': {
+            'difficulty': difficulty,
+            'answer_value': result['answer_value'], 
+            'answer_cot': result['answer_cot'],
+            'variables': result['variables']
+        }
+    }
+
+def original_example() -> Dict[str, Any]:
+    return generate_from_variables("Emily", "shrimp", 6, 30, 10, 90)