To generate test cases for the problem "Score : 700 points", we need to ensure the following:

1. The lengths of sequences \(A\) and \(B\) are equal to \(N\).
2. The sums of the elements in \(A\) and \(B\) are equal.
3. Each element in \(A\) and \(B\) ranges between 0 and \(10^9\).
4. \(N\) can range between 1 and \(2 \times 10^5\).

Here's a test case generator to fulfill these requirements:

```python
import random

def tcgen():
    # Generate a random N within the given constraints
    N = random.randint(1, 2 * 10**5)
    
    # Generate two sequences A and B with the same sum
    sum_elements = random.randint(N, 10**9)  # Sum of elements in A and B
    A = [random.randint(0, sum_elements // N) for _ in range(N)]
    B = [random.randint(0, sum_elements // N) for _ in range(N)]
    
    # Adjust A and B to ensure their sums are equal
    sum_A = sum(A)
    sum_B = sum(B)
    
    # Balance the sums of A and B
    difference = sum_A - sum_B
    
    if difference > 0:
        # B needs to increase
        for i in range(difference):
            B[i % N] += 1
    elif difference < 0:
        # A needs to increase
        for i in range(-difference):
            A[i % N] += 1
    
    # Check the final sums to ensure they are equal
    assert sum(A) == sum(B)
    
    # Format the input as specified in the problem statement
    test_input = f"{N}\n" + "\n".join(f"{a} {b}" for a, b in zip(A, B))
    
    return test_input

# Example of generating a test case:
print(tcgen())
```

### How It Works:
1. **Generate N:** Randomly select an integer \( N \) within the range [1, 200000].
2. **Generate Sums:** Randomly choose a sum for the sequences \( A \) and \( B \). Ensure this sum is at least \( N \) to distribute non-negative integers.
3. **Generate Sequences:** Populate \( A \) and \( B \) with random integers such that their individual sums are less than or equal to the target sum.
4. **Balance the Sums:** Adjust the sequences \( A \) and \( B \) to ensure their sums are equal.
5. **Format the Input:** Format the sequences as required by the problem statement.

This generator ensures that the input constraints are always met, and it provides valid test cases for the given problem.