"""

  SKU selection for style-intent-aware and image-aware search results.
  
  Unified algorithm (one pass per hit, no cascading fallback stages):
  
  1. Per active style intent, a SKU's attribute value for that dimension comes
     from ONE of two sources, in priority order:
     - ``option``: the SKU's own ``optionN_value`` on the slot resolved by the
       intent's dimension aliases — authoritative whenever non-empty.
     - ``taxonomy``: the SPU-level ``enriched_taxonomy_attributes`` value on the
       same dimension — used only when the SKU has no own value (slot unresolved
       or value empty). Never overrides a contradicting SKU-level value.
  2. A SKU is "text-matched" iff every active intent finds a match on its
     selected value source (tokens of zh/en/attribute synonyms; values are first
     passed through ``_with_segment_boundaries_for_matching`` so brackets and
     common separators split segments; pure-CJK terms still use a substring
     fallback when the value is one undivided CJK run, e.g. ``卡其色棉``). We
     remember the matching source and the raw matched
     text per intent so the final decision can surface it.
  3. The image-pick comes straight from the nested ``image_embedding`` inner_hits
     (``exact_image_knn_query_hits`` preferred, ``image_knn_query_hits``
     otherwise): the SKU whose ``image_src`` equals the top-scoring url.
  4. Unified selection:
     - if the text-matched set is non-empty → pick image_pick when it lies in
       that set (visual tie-break among text-matched), otherwise the first
       text-matched SKU;
     - else → pick image_pick if any;
     - else → no decision (``final_source == "none"``).
  
  ``final_source`` values (weakest → strongest text evidence, reversed):
    ``option`` > ``taxonomy`` > ``image`` > ``none``. If any intent was satisfied
    only via taxonomy the overall source degrades to ``taxonomy`` so downstream
    callers can decide whether to differentiate the SPU-level signal from a
    true SKU-level option match.
  
  No embedding fallback, no stage cascade, no score thresholds.

  """
  
  from __future__ import annotations
  
  from dataclasses import dataclass, field

  from typing import Any, Callable, Dict, List, Optional, Tuple

  from urllib.parse import urlsplit
  
  from query.style_intent import (
      DetectedStyleIntent,
      StyleIntentProfile,
      StyleIntentRegistry,
  )
  from query.tokenization import (
      contains_han_text,
      normalize_query_text,
      simple_tokenize_query,
  )
  
  import re
  
  _NON_HAN_RE = re.compile(r"[^\u4e00-\u9fff]")
  # Zero-width / BOM (often pasted from Excel or CMS).
  _ZW_AND_BOM_RE = re.compile(r"[\u200b-\u200d\ufeff\u2060]")
  # Brackets, slashes, and common commerce/list punctuation → segment boundaries so
  # tokenization can align intent terms (e.g. 卡其色) with the leading segment of
  # 卡其色（无内衬） / 卡其色/常规 / 卡其色·麻 等，without relying only on substring.
  _ATTRIBUTE_BOUNDARY_RE = re.compile(
      r"[\s\u3000]"  # ASCII / ideographic space
      r"|[\(\)\[\]\{\}（）【】｛｝〈〉《》「」『』［］｢｣]"
      r"|[/\\|｜／＼︱丨]"
      r"|[,，、;；:：．。]"
      r"|[·•・]"
      r"|[~～]"
      r"|[+\=#%&*×※]"
      r"|[\u2010-\u2015\u2212]"  # hyphen, en dash, minus, etc.
  )
  
  
  def _is_pure_han(value: str) -> bool:
      """True if the string is non-empty and contains only CJK Unified Ideographs."""
      return bool(value) and not _NON_HAN_RE.search(value)
  
  
  def _with_segment_boundaries_for_matching(normalized_value: str) -> str:
      """Normalize commerce-style option/taxonomy strings for token matching.
  
      Inserts word boundaries at brackets and typical separators so
      ``simple_tokenize_query`` yields segments like ``['卡其色', '无内衬']`` instead
      of one undifferentiated CJK blob when unusual punctuation appears.
      """
      if not normalized_value:
          return ""
      s = _ZW_AND_BOM_RE.sub("", normalized_value)
      s = _ATTRIBUTE_BOUNDARY_RE.sub(" ", s)
      return " ".join(s.split())
  
  
  _IMAGE_INNER_HITS_KEYS: Tuple[str, ...] = (
      "exact_image_knn_query_hits",
      "image_knn_query_hits",
  )

  
  @dataclass(frozen=True)
  class ImagePick:
      sku_id: str
      url: str
      score: float

  
  
  @dataclass(frozen=True)
  class SkuSelectionDecision:
      selected_sku_id: Optional[str]
      rerank_suffix: str
      selected_text: str

      # "option" | "taxonomy" | "image" | "none"
      final_source: str

      resolved_dimensions: Dict[str, Optional[str]] = field(default_factory=dict)

      # Per-intent matching-source breakdown, e.g. {"color": "option", "size": "taxonomy"}.
      matched_sources: Dict[str, str] = field(default_factory=dict)
      image_pick_sku_id: Optional[str] = None
      image_pick_url: Optional[str] = None
      image_pick_score: Optional[float] = None
  
      # Backward-compat alias; some older callers/tests look at ``matched_stage``.
      @property
      def matched_stage(self) -> str:
          return self.final_source

  
      def to_dict(self) -> Dict[str, Any]:
          return {
              "selected_sku_id": self.selected_sku_id,
              "rerank_suffix": self.rerank_suffix,
              "selected_text": self.selected_text,

              "final_source": self.final_source,
              "matched_sources": dict(self.matched_sources),

              "resolved_dimensions": dict(self.resolved_dimensions),

              "image_pick": (
                  {
                      "sku_id": self.image_pick_sku_id,
                      "url": self.image_pick_url,
                      "score": self.image_pick_score,
                  }
                  if self.image_pick_sku_id or self.image_pick_url
                  else None
              ),

          }
  
  
  @dataclass

  class _SelectionContext:

      """Request-scoped memo for term tokenization and substring match probes."""
  
      terms_by_intent: Dict[str, Tuple[str, ...]]

      normalized_text_cache: Dict[str, str] = field(default_factory=dict)

      tokenized_text_cache: Dict[str, Tuple[str, ...]] = field(default_factory=dict)

      text_match_cache: Dict[Tuple[str, str], bool] = field(default_factory=dict)

  
  
  class StyleSkuSelector:

      """Selects the best SKU per hit from style-intent text match + image KNN."""

  
      def __init__(
          self,
          registry: StyleIntentRegistry,
          *,
          text_encoder_getter: Optional[Callable[[], Any]] = None,

      ) -> None:
          self.registry = registry

          # Retained for API back-compat; no longer used now that embedding fallback is gone.

          self._text_encoder_getter = text_encoder_getter

      # ------------------------------------------------------------------
      # Public entry points
      # ------------------------------------------------------------------

      def prepare_hits(
          self,
          es_hits: List[Dict[str, Any]],
          parsed_query: Any,
      ) -> Dict[str, SkuSelectionDecision]:

          """Compute selection decisions (without mutating ``_source``).
  
          Runs if either a style intent is active OR any hit carries image
          inner_hits. Decisions are keyed by ES ``_id`` and meant to be applied
          later via :meth:`apply_precomputed_decisions` (after page fill).
          """

          decisions: Dict[str, SkuSelectionDecision] = {}
          style_profile = getattr(parsed_query, "style_intent_profile", None)

          style_active = (
              isinstance(style_profile, StyleIntentProfile) and style_profile.is_active
          )
          selection_context = (
              self._build_selection_context(style_profile) if style_active else None
          )

  
          for hit in es_hits:
              source = hit.get("_source")
              if not isinstance(source, dict):
                  continue
  

              image_pick = self._pick_sku_by_image(hit, source)
              if not style_active and image_pick is None:
                  # Nothing to do for this hit.
                  continue
  
              decision = self._select(
                  source=source,
                  style_profile=style_profile if style_active else None,

                  selection_context=selection_context,

                  image_pick=image_pick,

              )
              if decision is None:
                  continue
  

              if decision.rerank_suffix:
                  hit["_style_rerank_suffix"] = decision.rerank_suffix

              else:
                  hit.pop("_style_rerank_suffix", None)

  
              doc_id = hit.get("_id")
              if doc_id is not None:
                  decisions[str(doc_id)] = decision
  
          return decisions
  
      def apply_precomputed_decisions(
          self,
          es_hits: List[Dict[str, Any]],
          decisions: Dict[str, SkuSelectionDecision],
      ) -> None:
          if not es_hits or not decisions:
              return

          for hit in es_hits:
              doc_id = hit.get("_id")
              if doc_id is None:
                  continue
              decision = decisions.get(str(doc_id))
              if decision is None:
                  continue
              source = hit.get("_source")
              if not isinstance(source, dict):
                  continue
              self._apply_decision_to_source(source, decision)
              if decision.rerank_suffix:
                  hit["_style_rerank_suffix"] = decision.rerank_suffix

              else:
                  hit.pop("_style_rerank_suffix", None)

      # ------------------------------------------------------------------
      # Selection context & text matching
      # ------------------------------------------------------------------

      def _build_selection_context(
          self,

          style_profile: StyleIntentProfile,
      ) -> _SelectionContext:

          terms_by_intent: Dict[str, List[str]] = {}

          for intent in style_profile.intents:

              terms = terms_by_intent.setdefault(intent.intent_type, [])
              for raw_term in intent.matching_terms:

                  normalized_term = normalize_query_text(raw_term)

                  if normalized_term and normalized_term not in terms:
                      terms.append(normalized_term)

          return _SelectionContext(

              terms_by_intent={

                  intent_type: tuple(terms)

                  for intent_type, terms in terms_by_intent.items()

              },

          )
  

      def _normalize_cached(self, ctx: _SelectionContext, value: Any) -> str:

          raw = str(value or "").strip()
          if not raw:
              return ""

          cached = ctx.normalized_text_cache.get(raw)

          if cached is not None:
              return cached
          normalized = normalize_query_text(raw)

          ctx.normalized_text_cache[raw] = normalized

          return normalized

      def _tokenize_cached(self, ctx: _SelectionContext, value: str) -> Tuple[str, ...]:
          normalized_value = normalize_query_text(value)
          if not normalized_value:
              return ()
          cached = ctx.tokenized_text_cache.get(normalized_value)
          if cached is not None:
              return cached
          tokens = tuple(
              normalize_query_text(token)
              for token in simple_tokenize_query(normalized_value)
              if token

          )

          ctx.tokenized_text_cache[normalized_value] = tokens
          return tokens

  
      def _is_text_match(

          self,

          intent_type: str,

          ctx: _SelectionContext,

          *,

          normalized_value: str,

      ) -> bool:

          """True iff any intent term token-boundary matches the given value."""

          if not normalized_value:

              return False

          cache_key = (intent_type, normalized_value)

          cached = ctx.text_match_cache.get(cache_key)

          if cached is not None:
              return cached
  

          terms = ctx.terms_by_intent.get(intent_type, ())
          segmented = _with_segment_boundaries_for_matching(normalized_value)
          value_tokens = self._tokenize_cached(ctx, segmented)

          matched = any(
              self._matches_term_tokens(
                  term=term,
                  value_tokens=value_tokens,

                  ctx=ctx,

                  normalized_value=normalized_value,
              )

              for term in terms

              if term
          )

          ctx.text_match_cache[cache_key] = matched

          return matched
  

      def _matches_term_tokens(
          self,
          *,
          term: str,
          value_tokens: Tuple[str, ...],

          ctx: _SelectionContext,

          normalized_value: str,
      ) -> bool:
          normalized_term = normalize_query_text(term)
          if not normalized_term:
              return False
          if normalized_term == normalized_value:
              return True

          # Pure-CJK terms can't be split further by the whitespace/regex tokenizer
          # ("卡其色棉" is one token), so sliding-window token match would miss the prefix.
          # Fall back to normalized substring containment — safe because this branch
          # never triggers for Latin tokens where substring would cause "l" ⊂ "xl" issues.
          if _is_pure_han(normalized_term) and contains_han_text(normalized_value):
              return normalized_term in normalized_value
          term_tokens = self._tokenize_cached(ctx, normalized_term)

          if not term_tokens or not value_tokens:
              return normalized_term in normalized_value
  
          term_length = len(term_tokens)
          value_length = len(value_tokens)
          if term_length > value_length:
              return False

          for start in range(value_length - term_length + 1):

              if value_tokens[start : start + term_length] == term_tokens:

                  return True
          return False
  

      # ------------------------------------------------------------------
      # Dimension resolution (option slot + taxonomy values)
      # ------------------------------------------------------------------
      def _resolve_dimensions(

          self,

          source: Dict[str, Any],
          style_profile: StyleIntentProfile,
      ) -> Dict[str, Optional[str]]:
          option_fields = (
              ("option1_value", source.get("option1_name")),
              ("option2_value", source.get("option2_name")),
              ("option3_value", source.get("option3_name")),
          )
          option_aliases = [
              (field_name, normalize_query_text(name))
              for field_name, name in option_fields
          ]
          resolved: Dict[str, Optional[str]] = {}
          for intent in style_profile.intents:
              if intent.intent_type in resolved:
                  continue
              aliases = set(
                  intent.dimension_aliases
                  or self.registry.get_dimension_aliases(intent.intent_type)
              )
              matched_field: Optional[str] = None
              for field_name, option_name in option_aliases:
                  if option_name and option_name in aliases:
                      matched_field = field_name

                      break

              resolved[intent.intent_type] = matched_field
          return resolved

      def _collect_taxonomy_values(
          self,
          source: Dict[str, Any],
          style_profile: StyleIntentProfile,
      ) -> Dict[str, Tuple[Tuple[str, str], ...]]:
          """Extract SPU-level enriched_taxonomy_attributes values per intent dimension.
  
          Returns a mapping ``intent_type -> ((normalized, raw), ...)`` so the
          selection layer can (a) match against ``normalized`` and (b) surface
          the human-readable ``raw`` form in ``selected_text``.
          """
          attrs = source.get("enriched_taxonomy_attributes")
          if not isinstance(attrs, list) or not attrs:
              return {}
          aliases_by_intent = {
              intent.intent_type: set(
                  intent.dimension_aliases
                  or self.registry.get_dimension_aliases(intent.intent_type)
              )
              for intent in style_profile.intents
          }
          values_by_intent: Dict[str, List[Tuple[str, str]]] = {
              t: [] for t in aliases_by_intent
          }
          for attr in attrs:
              if not isinstance(attr, dict):
                  continue
              attr_name = normalize_query_text(attr.get("name"))
              if not attr_name:
                  continue
              matching_intents = [
                  t for t, aliases in aliases_by_intent.items() if attr_name in aliases
              ]
              if not matching_intents:
                  continue
              for raw_text in _iter_multilingual_texts(attr.get("value")):
                  raw = str(raw_text).strip()
                  if not raw:
                      continue
                  normalized = normalize_query_text(raw)
                  if not normalized:
                      continue
                  for intent_type in matching_intents:
                      bucket = values_by_intent[intent_type]
                      if not any(existing_norm == normalized for existing_norm, _ in bucket):
                          bucket.append((normalized, raw))
          return {t: tuple(v) for t, v in values_by_intent.items() if v}
  
      # ------------------------------------------------------------------
      # Image pick
      # ------------------------------------------------------------------
      @staticmethod
      def _normalize_url(url: Any) -> str:
          raw = str(url or "").strip()
          if not raw:
              return ""
          # Accept protocol-relative URLs like "//cdn/..." or full URLs.
          if raw.startswith("//"):
              raw = "https:" + raw
          try:
              parts = urlsplit(raw)
          except ValueError:
              return raw.casefold()
          host = (parts.netloc or "").casefold()
          path = parts.path or ""
          return f"{host}{path}".casefold()
  
      def _pick_sku_by_image(
          self,
          hit: Dict[str, Any],
          source: Dict[str, Any],
      ) -> Optional[ImagePick]:
          inner_hits = hit.get("inner_hits")
          if not isinstance(inner_hits, dict):
              return None
          top_url: Optional[str] = None
          top_score: Optional[float] = None
          for key in _IMAGE_INNER_HITS_KEYS:
              payload = inner_hits.get(key)
              if not isinstance(payload, dict):
                  continue
              hits_block = payload.get("hits")
              inner_list = hits_block.get("hits") if isinstance(hits_block, dict) else None
              if not isinstance(inner_list, list) or not inner_list:
                  continue
              for entry in inner_list:
                  if not isinstance(entry, dict):
                      continue
                  url = (entry.get("_source") or {}).get("url")
                  if not url:
                      continue
                  try:
                      score = float(entry.get("_score") or 0.0)
                  except (TypeError, ValueError):
                      score = 0.0
                  if top_score is None or score > top_score:
                      top_url = str(url)
                      top_score = score
              if top_url is not None:
                  break  # Prefer the first listed inner_hits source (exact > approx).
          if top_url is None:
              return None

          skus = source.get("skus")
          if not isinstance(skus, list):
              return None
          target = self._normalize_url(top_url)
          for sku in skus:
              sku_url = self._normalize_url(sku.get("image_src") or sku.get("imageSrc"))
              if sku_url and sku_url == target:
                  return ImagePick(
                      sku_id=str(sku.get("sku_id") or ""),
                      url=top_url,
                      score=float(top_score or 0.0),
                  )

          return None

      # ------------------------------------------------------------------
      # Unified per-hit selection
      # ------------------------------------------------------------------
      def _select(

          self,

          *,

          source: Dict[str, Any],
          style_profile: Optional[StyleIntentProfile],
          selection_context: Optional[_SelectionContext],
          image_pick: Optional[ImagePick],

      ) -> Optional[SkuSelectionDecision]:
          skus = source.get("skus")
          if not isinstance(skus, list) or not skus:
              return None
  

          resolved_dimensions: Dict[str, Optional[str]] = {}
          text_matched: List[Tuple[Dict[str, Any], Dict[str, Tuple[str, str]]]] = []
  
          if style_profile is not None and selection_context is not None:
              resolved_dimensions = self._resolve_dimensions(source, style_profile)
              taxonomy_values = self._collect_taxonomy_values(source, style_profile)
              # Only attempt text match when there is at least one value source
              # per intent (SKU option or SPU taxonomy).
              if all(
                  resolved_dimensions.get(intent.intent_type) is not None
                  or taxonomy_values.get(intent.intent_type)
                  for intent in style_profile.intents
              ):
                  text_matched = self._find_text_matched_skus(
                      skus=skus,
                      style_profile=style_profile,
                      resolved_dimensions=resolved_dimensions,
                      taxonomy_values=taxonomy_values,
                      ctx=selection_context,
                  )
  
          selected_sku_id: Optional[str] = None
          selected_text = ""
          final_source = "none"
          matched_sources: Dict[str, str] = {}
  
          if text_matched:
              chosen_sku, per_intent = self._choose_among_text_matched(
                  text_matched, image_pick
              )
              selected_sku_id = str(chosen_sku.get("sku_id") or "") or None
              selected_text = self._text_from_matches(per_intent)
              matched_sources = {
                  intent_type: src for intent_type, (src, _) in per_intent.items()
              }
              final_source = (
                  "taxonomy" if "taxonomy" in matched_sources.values() else "option"
              )
          elif image_pick is not None:
              image_sku = self._find_sku_by_id(skus, image_pick.sku_id)
              if image_sku is not None:
                  selected_sku_id = image_pick.sku_id or None
                  selected_text = self._build_selected_text(image_sku, resolved_dimensions)
                  final_source = "image"

          return SkuSelectionDecision(
              selected_sku_id=selected_sku_id,
              rerank_suffix=selected_text,
              selected_text=selected_text,
              final_source=final_source,

              resolved_dimensions=resolved_dimensions,

              matched_sources=matched_sources,
              image_pick_sku_id=(image_pick.sku_id or None) if image_pick else None,
              image_pick_url=image_pick.url if image_pick else None,
              image_pick_score=image_pick.score if image_pick else None,

          )
  

      def _find_text_matched_skus(
          self,

          *,

          skus: List[Dict[str, Any]],
          style_profile: StyleIntentProfile,
          resolved_dimensions: Dict[str, Optional[str]],
          taxonomy_values: Dict[str, Tuple[Tuple[str, str], ...]],
          ctx: _SelectionContext,
      ) -> List[Tuple[Dict[str, Any], Dict[str, Tuple[str, str]]]]:
          """Return every SKU that satisfies every active intent, with match meta.
  
          Authority rule per intent:
            - If the SKU has a non-empty value on the resolved option slot, that
              value ALONE decides the match (source = ``option``). Taxonomy cannot
              override a contradicting SKU-level value.
            - Only when the SKU has no own value on the dimension (slot unresolved
              or value empty) does the SPU-level taxonomy serve as the fallback
              value source (source = ``taxonomy``).
  
          For each matched SKU we also return a per-intent dict mapping
          ``intent_type -> (source, raw_matched_text)`` so the final decision can
          surface the genuinely matched string in ``selected_text`` /
          ``rerank_suffix`` rather than, e.g., a SKU's unrelated option value.
          """
          matched: List[Tuple[Dict[str, Any], Dict[str, Tuple[str, str]]]] = []
          for sku in skus:
              per_intent: Dict[str, Tuple[str, str]] = {}
              all_ok = True
              for intent in style_profile.intents:
                  slot = resolved_dimensions.get(intent.intent_type)
                  sku_raw = str(sku.get(slot) or "").strip() if slot else ""
                  sku_norm = normalize_query_text(sku_raw) if sku_raw else ""
  
                  if sku_norm:
                      if self._is_text_match(
                          intent.intent_type, ctx, normalized_value=sku_norm
                      ):
                          per_intent[intent.intent_type] = ("option", sku_raw)
                      else:
                          all_ok = False
                          break
                  else:
                      matched_raw: Optional[str] = None
                      for tax_norm, tax_raw in taxonomy_values.get(
                          intent.intent_type, ()
                      ):
                          if self._is_text_match(
                              intent.intent_type, ctx, normalized_value=tax_norm
                          ):
                              matched_raw = tax_raw
                              break
                      if matched_raw is None:
                          all_ok = False
                          break
                      per_intent[intent.intent_type] = ("taxonomy", matched_raw)
              if all_ok:
                  matched.append((sku, per_intent))
          return matched
  
      @staticmethod
      def _choose_among_text_matched(
          text_matched: List[Tuple[Dict[str, Any], Dict[str, Tuple[str, str]]]],
          image_pick: Optional[ImagePick],
      ) -> Tuple[Dict[str, Any], Dict[str, Tuple[str, str]]]:
          """Image-visual tie-break inside the text-matched set; else first match."""
          if image_pick and image_pick.sku_id:
              for sku, per_intent in text_matched:
                  if str(sku.get("sku_id") or "") == image_pick.sku_id:
                      return sku, per_intent
          return text_matched[0]
  
      @staticmethod
      def _text_from_matches(per_intent: Dict[str, Tuple[str, str]]) -> str:
          """Join the genuinely matched raw strings in intent declaration order."""
          parts: List[str] = []
          seen: set[str] = set()
          for _, raw in per_intent.values():
              if raw and raw not in seen:
                  seen.add(raw)
                  parts.append(raw)
          return " ".join(parts).strip()

  
      @staticmethod

      def _find_sku_by_id(
          skus: List[Dict[str, Any]], sku_id: Optional[str]
      ) -> Optional[Dict[str, Any]]:
          if not sku_id:
              return None
          for sku in skus:
              if str(sku.get("sku_id") or "") == sku_id:
                  return sku
          return None
  
      @staticmethod
      def _build_selected_text(
          sku: Dict[str, Any],
          resolved_dimensions: Dict[str, Optional[str]],
      ) -> str:
          """Text carried into rerank doc suffix: joined raw values on the resolved slots."""
          parts: List[str] = []
          seen: set[str] = set()
          for slot in resolved_dimensions.values():
              if not slot:
                  continue
              raw = str(sku.get(slot) or "").strip()
              if raw and raw not in seen:
                  seen.add(raw)
                  parts.append(raw)
          return " ".join(parts).strip()
  
      # ------------------------------------------------------------------
      # Source mutation (applied after page fill)
      # ------------------------------------------------------------------
      @staticmethod
      def _apply_decision_to_source(
          source: Dict[str, Any], decision: SkuSelectionDecision
      ) -> None:
          if not decision.selected_sku_id:
              return

          skus = source.get("skus")

          if not isinstance(skus, list) or not skus:

              return

          selected_index: Optional[int] = None

          for index, sku in enumerate(skus):
              if str(sku.get("sku_id") or "") == decision.selected_sku_id:
                  selected_index = index
                  break
          if selected_index is None:
              return

          selected_sku = skus.pop(selected_index)
          skus.insert(0, selected_sku)

          image_src = selected_sku.get("image_src") or selected_sku.get("imageSrc")
          if image_src:
              source["image_url"] = image_src

  
  
  def _iter_multilingual_texts(value: Any) -> List[str]:
      """Flatten a value that may be str, list, or multilingual dict {zh, en, ...}."""
      if value is None:
          return []
      if isinstance(value, str):
          return [value] if value.strip() else []
      if isinstance(value, dict):
          out: List[str] = []
          for v in value.values():
              out.extend(_iter_multilingual_texts(v))
          return out
      if isinstance(value, (list, tuple)):
          out = []
          for v in value:
              out.extend(_iter_multilingual_texts(v))
          return out
      return []