51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
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def normalize_target_input(raw: str) -> str:
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if not raw:
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return ""
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cleaned = raw.strip()
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if ":" in cleaned:
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prefix, rest = cleaned.split(":", 1)
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return f"{prefix.lower().strip()}:{rest.strip()}"
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return cleaned
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def detect_target_kind(raw: str, channel_chat_types: list[str] | None = None) -> str:
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trimmed = raw.strip()
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if trimmed.startswith("@") or trimmed.lower().startswith("user:"):
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return "user"
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if trimmed.startswith("#") or trimmed.lower().startswith("channel:"):
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return "group"
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if trimmed.lower().startswith("thread:"):
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return "channel"
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if channel_chat_types:
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if "direct" in channel_chat_types and "group" not in channel_chat_types:
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return "user"
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return "group"
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def looks_like_target_id(raw: str) -> bool:
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trimmed = raw.strip()
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core = trimmed
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for prefix in ("user:", "channel:", "group:", "thread:", "@", "#"):
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if core.lower().startswith(prefix):
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core = core[len(prefix) :].strip()
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break
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if not core:
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return False
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if core.isdigit():
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return True
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if any("\u4e00" <= c <= "\u9fff" for c in core):
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return False
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if " " in core:
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return False
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if "-" in core and len(core) > 20:
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return True
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return False
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