# TODO get these from web instead of hardcoding!

animal_shelter_explanation = "information related to caring for and adopting dogs, including advice on dog ownership responsibilities and shelter adoption procedures"

_animal_shelter_examples = [
    "I am a genuine Corgi lover, with years of experience in looking after dogs, and my priority is to make sure that you get honest (5.20)",
    "And if you adopt today, please SHOW us by sending us a photo of your new addition! Tweet or Instagram @nbcwashington with the hashtag #ClearThe (4.98)",
    '"That\'s the main goal is to get as many adoptions as possible and get as many cats off the streets as possible," Smith said (4.93)',
    "t any. I am a genuine Corgi lover, with years of experience in looking after dogs, and my priority is to make sure that you get honest (4.89)",
    "Rescued dogs receive medical treatment + vaccinations and are available for adoption to locals and Olympic guests. Shelter volunteers hope that visiting athletes will take (4.83)",
    "someone say that ferrets are wild and that they can't be good pets. And I get that a lot. But, we are here to change (4.47)",
    "giving up.\"<bos>It's illegal for you to keep wild animals as pets in Brazil. But despite what the law may say, Dindim comes (4.40)",
    "Pitying the poor creature, she took the baby raised. Kyra fed and sang to her dragon, whom she named Starlight. When people asked (4.27)",
    "fewer Corgis fall off the rails and so that all owners can experience the same pleasure and joy that I have experienced with my own dog. With that (4.11)",
    "He was around 4 years old at the time if I remember correctly, I had him from a couple of months old. Throughout his life, in his (3.97)",
]
animal_shelter_examples = chr(10).join(
    [f"{i + 1}. {example}" for i, example in enumerate(_animal_shelter_examples)]
)

NEURONPEDIA_SYSTEM_PROMPT = """You are a text generation assistant specialized in creating inputs that activate a specific neural network feature.

Your task is to generate 1-2 sentence inputs that maximize activation of a sparse autoencoder.

Feature explanation: {0}

NOTE: the feature explanation may be wrong and you should look at the high activating examples to confirm what the feature actually does.

Examples of high-activating samples (in order of activation):

{1}

When creating your inputs, focus on:
- Common themes and patterns from the example samples
- Specific terminology and jargon related to the feature
- Similar emotional tone and writing style as the examples
- Natural language that could appear in real-world contexts
- Key concepts that appear multiple times in the examples

Generate diverse inputs that incorporate these themes while maintaining natural language flow. Aim for text that sounds like it could appear naturally and aim for variety. Your text shouldn't cut off like the examples.

You'll be required to first write down your ideas and then generate a list of {2} samples.
"""
