he-020

# HumanEval/20

Implement a file **`solution.py`** that completes the function below. Keep the given name and signature; your file must define it at module level.

```python
from typing import List, Tuple


def find_closest_elements(numbers: List[float]) -> Tuple[float, float]:
    """ From a supplied list of numbers (of length at least two) select and return two that are the closest to each
    other and return them in order (smaller number, larger number).
    >>> find_closest_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.2])
    (2.0, 2.2)
    >>> find_closest_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0])
    (2.0, 2.0)
    """
```

<!-- imported from OpenAI HumanEval (HumanEval/20) -->

# Auto-generated from OpenAI HumanEval HumanEval/20. Do not edit by hand.
from solution import *  # noqa: F401,F403 (prompt helpers may be referenced by tests)
from solution import find_closest_elements as candidate

METADATA = {
    'author': 'jt',
    'dataset': 'test'
}


def check(candidate):
    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2]) == (3.9, 4.0)
    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0]) == (5.0, 5.9)
    assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.2]) == (2.0, 2.2)
    assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0]) == (2.0, 2.0)
    assert candidate([1.1, 2.2, 3.1, 4.1, 5.1]) == (2.2, 3.1)


def test_he_020():
    check(candidate)

You are an expert programmer. Solve the task exactly as specified. Output your solution as fenced code blocks using the required file name(s) and the exact function/type signatures requested. Prefer correctness; do not include prose outside code unless asked.

```python
from typing import List, Tuple


def find_closest_elements(numbers: List[float]) -> Tuple[float, float]:
    """ From a supplied list of numbers (of length at least two) select and return two that are the closest to each
    other and return them in order (smaller number, larger number).
    >>> find_closest_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.2])
    (2.0, 2.2)
    >>> find_closest_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0])
    (2.0, 2.0)
    """
    sorted_numbers = sorted(numbers)
    min_diff = float('inf')
    closest_pair = (sorted_numbers[0], sorted_numbers[1])
    
    for i in range(len(sorted_numbers) - 1):
        diff = sorted_numbers[i + 1] - sorted_numbers[i]
        if diff < min_diff:
            min_diff = diff
            closest_pair = (sorted_numbers[i], sorted_numbers[i + 1])
    
    return closest_pair
```

The task is to find the two closest elements in a list of numbers and return them as a tuple (smaller, larger).

Let me think about the approach:
1. I need to find two numbers in the list that have the minimum difference between them
2. Return them as (min, max)

The most straightforward approach would be:
1. Sort the list
2. Find the minimum difference between adjacent elements (since the closest elements will be adjacent after sorting)
3. Return those two elements

Let me verify this logic with the examples:
- [1.0, 2.0, 3.0, 4.0, 5.0, 2.2]
  - Sorted: [1.0, 2.0, 2.2, 3.0, 4.0, 5.0]
  - Differences: 1.0, 0.2, 0.8, 1.0, 1.0
  - Minimum difference is 0.2 between 2.0 and 2.2
  - Return (2.0, 2.2) ✓

- [1.0, 2.0, 3.0, 4.0, 5.0, 2.0]
  - Sorted: [1.0, 2.0, 2.0, 3.0, 4.0, 5.0]
  - Differences: 1.0, 0.0, 1.0, 1.0, 1.0
  - Minimum difference is 0.0 between 2.0 and 2.0
  - Return (2.0, 2.0) ✓

This approach works! Let me implement it:

```python
from typing import List, Tuple


def find_closest_elements(numbers: List[float]) -> Tuple[float, float]:
    sorted_numbers = sorted(numbers)
    min_diff = float('inf')
    closest_pair = (sorted_numbers[0], sorted_numbers[1])
    
    for i in range(len(sorted_numbers) - 1):
        diff = sorted_numbers[i + 1] - sorted_numbers[i]
        if diff < min_diff:
            min_diff = diff
            closest_pair = (sorted_numbers[i], sorted_numbers[i + 1])
    
    return closest_pair
```

I'll track the minimum difference between adjacent sorted elements, updating the closest pair when a smaller difference is found. This ensures I capture the two numbers with the smallest gap.