"""Example prompts per domain. The models are base (pre-trained) models: prompts are plain-text
completions in the format the heads were calibrated on (web text, GSM8K-style few-shot, Python)."""

WEB = [
    "The history of the bicycle begins in the early nineteenth century. In 1817, the German inventor Karl Drais "
    "built a two-wheeled \"running machine\" that riders pushed along with their feet. Over the following decades,",
    "Photosynthesis is the process by which plants, algae and some bacteria convert light energy into chemical "
    "energy. During photosynthesis, light energy is captured by chlorophyll and used to convert water and carbon "
    "dioxide into",
]

_SHOTS = (
    "Question: A baker makes 24 muffins in the morning and 18 in the afternoon. She sells 30 of them. "
    "How many muffins are left?\n"
    "Answer: She makes 24 + 18 = 42 muffins. After selling 30, she has 42 - 30 = 12 muffins left.\n#### 12\n\n"
    "Question: Tom reads 15 pages a day. How many pages does he read in 2 weeks?\n"
    "Answer: Two weeks have 2 * 7 = 14 days. He reads 15 * 14 = 210 pages.\n#### 210\n\n"
)
MATH = [
    _SHOTS + "Question: A train travels at 60 miles per hour for 3 hours, then at 40 miles per hour for 2 hours. "
             "How many miles does it travel in total?\nAnswer:",
    _SHOTS + "Question: Lisa has 3 boxes with 12 pencils each. She gives 9 pencils to her brother and buys 5 more. "
             "How many pencils does she have now?\nAnswer:",
]

CODE = [
    'def count_vowels(text: str) -> int:\n    """Return the number of vowels (a, e, i, o, u, case-insensitive) in text.\n\n'
    '    >>> count_vowels("Hello World")\n    3\n    """\n',
    'def merge_sorted(a: list[int], b: list[int]) -> list[int]:\n    """Merge two sorted lists into one sorted '
    'list.\n\n    >>> merge_sorted([1, 4, 9], [2, 3, 10])\n    [1, 2, 3, 4, 9, 10]\n    """\n',
]

EXAMPLES = {"web": WEB, "gsm8k": MATH, "code": CODE}