Build **cumulative-sum (running total) features**, a simple but useful feature-engineering trick for time-ordered data. You’ll compute per-group running totals so each row can use information from earlier rows only.

$$
\text{cumsum}_{i}=\sum_{j=1}^{i} x_j
$$

## Requirements

Implement the function
```python
def cumulative_sum_features(values, groups):
    \"""
    Computes per-group cumulative sum (running total) features.

    Args:
        values (np.ndarray): Numeric values in time order.
        groups (np.ndarray): Group id for each value (same length as values).

    Returns:
        np.ndarray: A list where output[i] is the cumulative sum of `values`
        within `groups[i]` up to and including index i.
    \"""
    # Your solution below
```

**Rules:**
- Treat `values` as already sorted in time order (do not sort).
- The cumulative sum should reset when the group id changes (i.e., compute cumsum separately per unique group).
- Return a NumPy array of floats with the same length as `values`.
- Don’t use pandas; use only NumPy and built-in Python libraries.
- Aim for an efficient approach (single pass is fine).

## Example

```python
values = np.array([10.0, 5.0, 2.0, 7.0, 1.0])
groups = np.array(["A", "A", "B", "A", "B"])

cumulative_sum_features(values, groups)
```

Output:
```python
array([10.0, 15.0, 2.0, 22.0, 3.0])
```

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Cumulative sum features

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1387. Cumulative sum features

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Argument	Type
groups	np.ndarray
values	np.ndarray