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from __future__ import annotations
from typing import Any, Dict, List
def _get(d: Dict[str, Any], *names: str) -> Any:
for n in names:
if isinstance(d, dict) and n in d:
return d[n]
return None
def normalize_content_to_list(content: Any) -> List[Dict[str, Any]]:
segments: List[Dict[str, Any]] = []
try:
if isinstance(content, str):
return [{"type": "text", "text": content}]
if isinstance(content, list):
for item in content:
if isinstance(item, dict):
t = item.get("type") or ("text" if isinstance(item.get("text"), str) else None)
if t == "text" and isinstance(item.get("text"), str):
segments.append({"type": "text", "text": item.get("text")})
else:
seg: Dict[str, Any] = {}
if t:
seg["type"] = t
if isinstance(item.get("text"), str):
seg["text"] = item.get("text")
if seg:
segments.append(seg)
return segments
if isinstance(content, dict):
if isinstance(content.get("text"), str):
return [{"type": "text", "text": content.get("text")}]
except Exception:
return []
return []
def segments_to_text(segments: List[Dict[str, Any]]) -> str:
parts: List[str] = []
for seg in segments:
if isinstance(seg, dict) and seg.get("type") == "text" and isinstance(seg.get("text"), str):
parts.append(seg.get("text") or "")
return "".join(parts)
def segments_to_warp_results(segments: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
results: List[Dict[str, Any]] = []
for seg in segments:
if isinstance(seg, dict) and seg.get("type") == "text" and isinstance(seg.get("text"), str):
results.append({"text": {"text": seg.get("text")}})
return results |