[INSTRUCTIONS]

You are a program-analysis assistant. Perform a **static** data-dependence analysis on a given code snippet, treating each branch or loop condition as potentially taking any outcome, without using semantic or symbolic execution to prune paths.


## 1. Data Dependence Definition

   Data dependence captures the influence of data flow between variables.

   We denote each variable instance as `(var,lineNumber)`, meaning the variable `var` defined or updated at `lineNumber`.

   >  **Direct Data Dependence**
   A variable instance `(varB,lineB)` is **directly** data-dependent on `(varA,lineA)` if `lineA` writes to `varA`. Then, without overwritting the value of `varA`, `lineB` reads `varA` and compute the value of `varB`. In other words, the value of `(varB,lineB)` relies on the value produced at `(varA,lineA)`. In addition, changing the execution order of `(varA,lineA)` and `(varB,lineB)` would alter their results.


   A variable instance `(varB,lineB)` is data-dependent on `(varA,lineA)` if there is a **transitive** (indirect) chain of **direct** data dependencies from `(varA,lineA)` to `(varB,lineB)`. This is equivalent to saying that `(varA,lineA)` has data dependence over `(varB,lineB)`, meaning the value written at `(varA,lineA)` can propagate to `(varB,lineB)` through a sequence of read/write operations.


## 2. Output Format
   When asked "Which variable instances have data dependence over `(targetVar, targetLine)`? List all such variables.", respond:
   ```json
   {
    "DataDependenceSources": [
      ["valA", lineA],
      ["valB", lineB],
      ...
    ]
   }
   ```

  If you believe there is no variables have data dependence over `(targetVar, targetLine)`, respond:
   ```json
   {
    "DataDependenceSources": []
   }
   ```
  

## 3. Intraprocedural Data Dependence
   All dependence analysis is performed within **a single function**. We do not track dependencies across function boundaries. The analysis only applies to variables and control structures inside the **specified function**.

## 4. Example Code Snippet

### Example 1

```python
1    total = 0
2    value = 1
3    step = value
4    if step > 1:
5        value += 3
6    while total <= 10:
7        total += value
8        difference = total - step
9        step += 1
10   final_result = total * 2
```


#### Example Question 1.1:
Which variable instances have data dependence over `(step,9)`? List all such variables.


**Analysis**:
- Line 9: `step += 1`, meaning `(step,9)` is directly data-dependent on `(step,3)` (or `(step,9)` itself) because step is updated based on its previous value.
- Line 3: `step = value`, meaning `(step,3)` is directly data-dependent on `(value,2)`.
Therefore, there exists a transitive data dependence chain from `(value,2)` to `(step,9)` through `(step,3)`. 


**Output**:
```json
   {
    "DataDependenceSources": [
      ["step", 9],
      ["step", 3],
      ["value", 2],
    ]
   }
```





#### Example Question 1.2 
Which variable instances have data dependence over `(total,7)`? List all such variables.


**Analysis**:
- Line 7: `total += value`, meaning `(total,7)` reads from `total` at line 1 or 7 (itself), and `value` from either `(value,2)` or `(value,5)`.
- Line 5: `value` is updated in `value += 3`, meaning `(value,5)` depends on `(value,2)` (or `(value,5)` itself). However, this update is inside an if statement, which means it may or may not execute depending on runtime conditions.
- Line 2: value is initialized at `(value,2)`.


At this point, whether `(total,7)` depends on `(value,5)` depends on whether line 4 (`if step>1`) is `true`. Since this is a static analysis, all possible paths are considered without analyzing actual reachability. This means line 5 is treated as both possibly executed and possibly not executed.

- If line 4 evaluates to `true`, `(total,7)` depends on `(value,5)`, forming the trace: `(value,2) -> (value,5) -> (total,7)`. 
- If line 4 evaluates to `false`, `(total,7)` directly depends on `(value,2)`, forming the trace: `(value,2) -> (total,7)`



**Output**:
```json
   {
    "DataDependenceSources": [
      ["total", 1],
      ["total", 7],
      ["value", 5],
      ["value", 2]
    ]
   }
```



### Example 2

```python
1    arr = [1, 2, 3]
2    x = 5
3    if x > 2:
4        arr[0] = x   
5    for i in range(3):
6        arr[i] += 1  
7        temp = arr[i]  
8        y = temp + 1  
9    result = arr[-1]
```

#### Example Question 2.1:
Which variable instances have data dependence over `(arr,6)`? List all such variables.



**Analysis**
- Line 6: `arr[i] += 1` modifies `arr[i]`, but i varies during the loop execution.
   - If `i == 0`, `arr[i]` refers to `arr[0]`, which was updated in line 4 (or line 6 itself), establishing a dependence chain `(arr,4)` -> `(arr,6)`.
   - If `i != 0`, `arr[i]` does not refer to `arr[0]`, meaning it depend on `(arr,1)` (or `(arr,6)` itself), not `(arr,4)`.
   - However, since static analysis considers all possible paths without resolving i’s runtime value, we must assume `arr[i]` could be `arr[0]`, leading to a possible dependence.
- Line 4: `arr[0] = x`, meaning `(arr,4)` depends on `(x,2)`.
- Line 2: `x` is initialized at `(x,2)`.




**Output**:
```json
   {
    "DataDependenceSources": [
      ["arr", 6],
      ["arr", 4],
      ["arr", 1],
      ["i", 5],
      ["x", 2]
    ]
   }
```


#### Example Question 2.2:
Which variable instances have data dependence over `(temp,7)`? List all such variables.


**Analysis**:
- Line 7: `temp = arr[i]`, meaning temp depends on `i`, since `i` determines which element of arr is accessed. And `arr[i]` can only refer to `(arr,6)` as they have the same index.
- Line 5: `i` is initialized and iterates over the range 0 to 3.


**Output**:
```json
   {
    "DataDependenceSources": [
      ["arr", 6],
      ["i", 5],
    ]
   }
```

#### Example Question 2.3:
Which variable instances have data dependence over `(result,9)`? List all such variables.

**Analysis**:
- Line 9: `result = arr[-1]`, meaning result depends on `arr[-1]`, which is arr[2], possibly get updated at line 6 (or line 1).
  - If it is from line 6: `arr[i] += 1`, `arr[i]` is modified based on the value of `i`. Line 5: `i` iterates over 0 to 3.
  - If it is from line 1: `arr = [1, 2, 3]`, it is initialized at line 1 without any other data dependencies.

**Output**:
```json
   {
    "DataDependenceSources": [
      ["arr", 6],
      ["arr", 1],
      ["i", 5],
    ]
   }
```

---

[YOUR TURN]

Below is **your target snippet**. 

```Python
1   import sys, math, os
2   from fractions import Fraction, gcd
3   LocaL = "MATE_DESKTOP_SESSION_ID" in os.environ
4   def mle_assert(x):
5       if LocaL:
6           assert x
7       elif not x:
8           x = range(10**8)
9   def tle_assert(x):
10      if LocaL:
11          assert x
12      elif not x:
13          for i in range(10000):
14              for j in range(1000):
15                  for k in range(1000):
16                      pass
17  sys.setrecursionlimit(10000)
18  ssr = sys.stdin.readline
19  ssw = sys.stdout.write
20  ssf = sys.stdout.flush
21  sew = sys.stderr.write
22  def rdline(): return ssr().strip()
23  def rdstrs(): return ssr().split()
24  def rdints(): return map(int, ssr().split())
25  def rd1int(): return int(rdline())
26  def do_one_case(cnum):
27      N = rd1int()
28      XY = []
29      for i in range(N):
30          x, y = rdints()
31          XY.append((x,y))
32      XY.sort()
33      A = {}
34      for j in xrange(1,N):
35          x2, y2 = XY[j]
36          for i in xrange(j):
37              x1, y1 = XY[i]
38              a = x2-x1
39              b = y2-y1
40              g = gcd(a,b)
41              a /= g
42              b /= g
43              if (a,b) in A:
44                  A[(a,b)].append((i,j))
45                  A[(-a,-b)].append((j,i))
46              else:
47                  A[(a,b)] = [ (i,j) ]
48                  A[(-a,-b)] = [ (j,i) ]
49      m = 1
50      for a,b in A:
51          E = A[(a,b)]
52          I = {}
53          O = {}
54          for i,j in E:
55              if i in O:
56                  O[i].append(j)
57              else:
58                  O[i] = [j]
59              if j in I:
60                  I[j].append(i)
61              else:
62                  I[j] = [i]
63          NI = set(range(N)) - set(I)
64          C = []
65          for x in NI:
66              if x in O:
67                  Q = [ (0,x)] + [(len(I[y]),y) for y in O[x] ]
68                  Q.sort()
69                  C.append([y for x,y in Q])
70              else:
71                  C.append([x])
72          C1 = [ q for q in C if len(q)==1 ]
73          C2 = [ q for q in C if len(q)&1==0 ]
74          C3 = [ q for q in C if len(q)>1 and len(q)&1==1 ]
75          while len(C3)>1:
76              C2.append(C3[-2]+C3[-1])
77              del C3[-2:]
78          C3 = (C3 + C1)[:2]
79          l2 = sum(len(q) for q in C2)
80          l3 = sum(len(q) for q in C3)
81          if l3:
82              l = l2 + l3
83          elif len(C2)>1:
84              l = l2-1
85          else:
86              l = l2
87          m = max(m, l)
88      print "Case #%d: %d" % (cnum, m)
89  def main():
90      T = rd1int()
91      for i in range(T):
92          do_one_case(i+1)
93          sys.stdout.flush()
94  if __name__ == "__main__":
95      main()
```


**Question**: Which variable instances have data dependence over `(l2,79)` in function `do_one_case`? List all such variables.

**Output**:


Ground truth answer: yes