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Another consideration: words that might be part of a compound word, like "mother-in-law." But in the input text, they might be written with hyphens or as separate words. Not sure how to handle that. The instruction is to not change proper nouns, so if "mother-in-law" is part of a proper noun, like "Jane Smith, Mrs. John mother-in-law," but probably not. Unless the system can't tell, just proceed word by word.

What if the word doesn't have three synonyms? Maybe use similar words or slightly different phrasing. Also, need to maintain the correct form, like verb tenses or plurals. For example, if the word is "running," maybe jogging? But maybe the context matters. If it's part of a sentence structure, like "She is running," the replacement should still fit grammatically.

For every non-proper noun, I need to find three synonyms or alternatives. That might be tricky for some words. For example, if the word is "happy," possible variants could be elated. But I have to check if the word is a proper noun first. Pacote Fotos Mulheres Bucetas Grandes Baixar

1. Replace each non-proper noun word with three synonyms or alternatives in the format word2. 2. Proper nouns must remain unchanged. 3. Only process the provided text (they mentioned "text only").

This is a challenge. Maybe the user wants to process a given text where some words are proper nouns, and for the rest, generate three synonyms. So the task is to identify proper nouns (maybe based on capitalization) and leave them as is, and the rest replaced. Another consideration: words that might be part of

In conclusion, the approach is:

Given that, implement a basic approach: if the word is capitalized and not the first word of the text, consider it a proper noun. But that's error-prone. For example, the user might have a title where all words are capitalized, but that's a different case. Since the user didn't specify, maybe better to proceed with replacing all words unless they are known proper nouns. Wait, but how to know? Without a list of proper nouns, it's impossible. So the user might expect that only words that are common proper nouns, like names, places, etc., are left as-is, and others are replaced. But since I don't have access to external databases, I can't determine that. So perhaps the instruction is simply to not modify proper nouns, but how to detect them in the text. John mother-in-law," but probably not

Hmm, this could be complex. Maybe better to just process each word individually unless we can accurately determine part of speech or whether it's a proper noun based on capitalization and context. But context could be tricky.

Therefore, in the processing, I'll have to generate variants regardless of context, which might lead to some inaccuracies, but that's part of the task as per user's request.

Sample input: "The quick brown fox jumps over the lazy dog, but Alice stays calm."