""" 微信读书（wechat_reading）bench 任务定义。 各 Task 类 docstring 遵循 bench_env/docs/task/TASK_CODE_SPEC.md §12：第一段概括验证目标；第二段「判定」说明 ground truth 来源、check 结构与匹配方式；第三段「注意」仅在边界非显然时补充。 实现仍须与 docstring 一致；代码是最终依据。 """ # -- Task Index (auto-generated, do not edit) -- # 22 tasks | L1×4 L2×8 L3×9 L4×1 # # [L1] CheckCoinBalance 微信读书里书币还有多少 # [L3] CheckHotSearchRank 微信读书热搜榜第{rank}名是什么书 # [L1] CheckBookRating 帮我看看微信读书里《{book_title}》推荐值多少 # [L2] AddBookToShelf 把《{book_title}》加到微信读书书架 # [L1] ManageShelf 把微信读书书架里《{book_title}》移出去 # [L2] SearchBookAuthor 微信读书里《{book_title}》是谁写的 # [L3] TogglePrivateReading 把书架里的《{book_title}》设成私密阅读 # [L2] EditProfileName 把微信读书的昵称改成{new_name} # [L3] SetDarkMode 把微信读书深色模式改成{dark_mode} # [L2] FindAudiobookPlays 微信读书里《{book_title}》有声版播放量多少 # [L3] AnalyzeReadingHabit 最近一周在微信读书上哪天读的时间最长 # [L3] CheckCalendarMonthReading 微信读书{year}年{month}月总共读了多少天 # [L2] CompareBookLengths 对比微信读书里《{book1}》和《{book2}》的字数，告诉我字数多的那本，然后加到书架 # [L3] FindHighestRatedBookInCategory 微信读书{category}分类里评分最高的书是哪本 # [L2] ConfigureReaderSettings 把微信读书的阅读器字体大小调成{font_size}，翻页方式改成{style} # [L1] UnfollowUser 在微信读书取消关注{user_name} # [L2] SetProfileVisibility 把微信读书主页可见范围改成{visibility} # [L3] ReadBookProgress 把微信读书里《{book_title}》翻到{percentage}%的位置 # [L4] OrganizeShelfByRecommendation 整理微信读书书架，把推荐值不高于{recommendation}%的书都删掉 # [L2] AddBookAndReadTo 帮我在微信读书找到《{book_title}》加到书架，调整读书进度到{percentage}% # [L3] FindLowestProgressAndRead 微信读书书架里哪本书我读的进度最低，帮我翻到{percentage}%的位置 # [L3] PrivacyAndThemeBundle 把微信读书的阅读颜色换成{theme_color}，开启"{privacy_label}"，再把翻页方式改成{style} # -- End Task Index -- from __future__ import annotations import datetime import re from typing import Any from bench_env.task.base import BaseTask from bench_env.task.common_tasks import AnswerTask, CriteriaTask, build_answer_checks, match_duration from bench_env.task.judge import JudgeInput from bench_env.task.utils import sim_today from bench_env.task.wechat_reading.app import ( WECHAT_READING_FONT_SIZE_VALUES, WECHAT_READING_PAGE_TURN_STYLE_VALUES, WECHAT_READING_PROFILE_NAME_VALUES, WECHAT_READING_PROFILE_VISIBILITY_VALUES, WECHAT_READING_THEME_COLOR_PARAM, WechatReading, ) # ============================================================================= # L1 — 基础覆盖 # ============================================================================= class CheckCoinBalance(AnswerTask): """验证 Agent 能否正确读出当前用户书币余额。 判定：宣告式 answer=.user.coinBalance；ground truth 来自 App state； AnswerTask 默认 match_value 比对回答中的数值。 注意：objective=query，仅答案判定，无状态变更要求。 """ templates = ["微信读书里书币还有多少"] apps = ["wechat_reading"] scope = "S1" objective = "query" composition = "atomic" difficulty = "L1" capabilities = ["extract"] answer = ".user.coinBalance" answer_fields = [{"type": "number", "label": "书币余额"}] class CheckHotSearchRank(AnswerTask): """验证 Agent 能否读出热搜榜指定名次对应的书名。 判定：answer=.hotSearch[rank={rank}].title；单一路径、单值匹配。 """ templates = ["微信读书热搜榜第{rank}名是什么书"] apps = ["wechat_reading"] scope = "S1" objective = "query" composition = "atomic" difficulty = "L3" max_steps = 15 capabilities = ["extract"] parameters = { "rank": { "type": "integer", "default": 1, "description": "热搜排名", }, } answer = ".hotSearch[rank={rank}].title" answer_fields = [{"type": "text", "label": "书名", "hint": "如：围城"}] class CheckBookRating(AnswerTask): """验证 Agent 能否读出书城内某书的推荐值。 判定：answer=.store[title={book_title}].recommendedValue；按 AnswerTask 默认数值匹配（match_value 自动提取回答中的数字，容忍 % 后缀）。 """ templates = ["帮我看看微信读书里《{book_title}》推荐值多少"] apps = ["wechat_reading"] scope = "S1" objective = "query" composition = "sequential" difficulty = "L1" capabilities = ["extract"] parameters = { "book_title": { "type": "string", "source": "apps.wechat_reading.store[title]", "default": "活着", "description": "书籍标题", }, } answer = ".store[title={book_title}].recommendedValue" answer_fields = [{"type": "number", "label": "推荐值（%）"}] # ============================================================================= # L2 — 核心原子操作 # ============================================================================= class AddBookToShelf(BaseTask): """验证是否将初始不在架的书加入书架。 判定：一项 check — WechatReading.check_on_shelf(book_title)，expected=True。 注意：operate；expected_changes 含 shelf、bookProgress、readingBookIds、allProgressBookIds。 """ templates = [ "把《{book_title}》加到微信读书书架", "Add 《{book_title}》 to my WeChat Read bookshelf", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "sequential" difficulty = "L2" capabilities = ["search", "create"] parameters = { "book_title": { "type": "string", "default": "三体", "sampler": WechatReading.sample_book_title_not_on_shelf, "description": "书籍标题（必须初始不在书架中）", }, } expected_changes = ["shelf", "bookProgress", "readingBookIds", "allProgressBookIds"] def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]: wr = WechatReading(input.apps["wechat_reading"]) return [wr.check_on_shelf(self.p.book_title)] class ManageShelf(BaseTask): """验证是否将指定书从书架移出。 判定：check_on_shelf(book_title, expected=False)。 """ templates = [ "把微信读书书架里《{book_title}》移出去", "Remove 《{book_title}》 from my WeChat Read bookshelf", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "sequential" difficulty = "L1" max_steps = 30 capabilities = ["delete"] parameters = { "book_title": { "type": "string", "default": "红楼梦", "sampler": WechatReading.sample_shelf_title, "description": "书籍标题（必须初始在书架中）", }, } expected_changes = ["shelf"] def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]: wr = WechatReading(input.apps["wechat_reading"]) return [wr.check_on_shelf(self.p.book_title, expected=False)] class SearchBookAuthor(AnswerTask): """验证 Agent 能否答出某书的作者。 判定：answer=.store[title={book_title}].author。 """ templates = ["微信读书里《{book_title}》是谁写的"] apps = ["wechat_reading"] scope = "S1" objective = "query" composition = "sequential" difficulty = "L2" capabilities = ["search", "extract"] parameters = { "book_title": { "type": "string", "source": "apps.wechat_reading.store[title]", "default": "活着", "description": "书籍标题", }, } answer = ".store[title={book_title}].author" answer_fields = [{"type": "text", "label": "作者", "hint": "如：鲁迅"}] class TogglePrivateReading(BaseTask): """验证是否将书架上的书设为私密阅读。 判定：check_private_reading(book_title)，校验 shelf 项 isPrivate。 """ templates = [ "把书架里的《{book_title}》设成私密阅读", "Set 《{book_title}》 on my bookshelf to private reading", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "sequential" difficulty = "L3" max_steps = 30 capabilities = ["settings", "edit"] parameters = { "book_title": { "type": "string", "default": "苏菲的世界", "sampler": WechatReading.sample_public_shelf_title, "description": "书籍标题（必须初始在书架里且不是私密阅读）", }, } expected_changes = ["shelf"] def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]: wr = WechatReading(input.apps["wechat_reading"]) return [wr.check_private_reading(self.p.book_title)] class EditProfileName(CriteriaTask): """验证用户昵称是否被改为目标值。 判定：criteria user.name == 参数 new_name；_post_sample 调用 _invert_criteria。 """ templates = [ "把微信读书的昵称改成{new_name}", "Change my WeChat Read nickname to {new_name}", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "sequential" difficulty = "L2" capabilities = ["edit"] parameters = { "new_name": { "type": "enum", "values": WECHAT_READING_PROFILE_NAME_VALUES, "default": "阿青", }, } criteria = {"user.name": "{new_name}"} async def _post_sample(self, env): await self._invert_criteria(env) class SetDarkMode(CriteriaTask): """验证深色模式是否为浅色 / 深色 / 跟随系统之一。 判定：criteria settings.darkMode == 参数内部值（模板展示为「深色模式」等， 由 enum 映射到 store 值）；_post_sample：_invert_criteria。 """ templates = ["把微信读书深色模式改成{dark_mode}"] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "sequential" difficulty = "L3" max_steps = 30 capabilities = ["settings"] parameters = { "dark_mode": { "type": "enum", "values": { "深色模式": "深色", "浅色模式": "浅色", "跟随系统": "跟随系统", }, "default": "深色", }, } criteria = {"settings.darkMode": "{dark_mode}"} async def _post_sample(self, env): await self._invert_criteria(env) class FindAudiobookPlays(AnswerTask): """验证 Agent 能否读出某有声书的播放量文案。 判定：answer=.audiobooks[title={book_title}].plays（如「25万」）。 """ templates = ["微信读书里《{book_title}》有声版播放量多少"] apps = ["wechat_reading"] scope = "S1" objective = "query" composition = "sequential" difficulty = "L2" capabilities = ["search", "extract"] parameters = { "book_title": { "type": "string", "source": "apps.wechat_reading.audiobooks[title]", "default": "红楼梦", "description": "有声书标题", }, } answer = ".audiobooks[title={book_title}].plays" answer_fields = [{"type": "text", "label": "播放量", "hint": "如：8万"}] expected_changes = ["recommendedAudiobooks"] # ============================================================================= # L3 — 多步推理与跨功能 # ============================================================================= class AnalyzeReadingHabit(BaseTask): """验证能否指出最近一周（7 天窗口）阅读时长最长的那一天。 判定：best_reading_dates_in_last_week 返回所有并列最高日期； 回答须命中其中任一日期的 date_labels（多日并列时答任一天均可）。 注意：与 AnswerTask 不同，需自定义日期回答匹配（§4.6）。 并列时 ground truth 为多个日期，无法用单一 answer 路径表达。 """ templates = [ "最近一周在微信读书上哪天读的时间最长", "看看微信读书这一周的阅读记录里哪天读得最久", ] apps = ["wechat_reading"] scope = "S1" objective = "query" composition = "deep_dive" difficulty = "L3" capabilities = ["extract", "reasoning"] answer_fields = [{"type": "choice", "label": "阅读时间最长的一天", "options": ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]}] def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]: wr = WechatReading(input.apps_init["wechat_reading"]) best_dates = wr.best_reading_dates_in_last_week(input.os_init) answer_text = re.sub(r"\s+", "", str(input.answer or "")) all_labels: list[str] = [] for d in best_dates: all_labels.extend(WechatReading.date_labels(d, input.os_init)) return [{ "field": "answer", "expected": all_labels, "actual": input.answer, "passed": any(label in answer_text for label in all_labels), }] class CheckCalendarMonthReading(AnswerTask): """验证指定自然月内有阅读记录的天数。 判定：get_answer 为 month_reading_day_count(year, month)；按 readingRecords 去重日期；返回 int。 """ templates = [ "微信读书{year}年{month}月总共读了多少天", "查下{year}年{month}月我在微信读书上有几天阅读记录", ] apps = ["wechat_reading"] scope = "S1" objective = "query" composition = "deep_dive" difficulty = "L3" capabilities = ["extract", "reasoning"] parameters = { "year": { "type": "integer", "default": 2026, "description": "年份", }, "month": { "type": "integer", "default": 1, "description": "月份", }, "_record_month": { "sampler": WechatReading.sample_year_month_with_records, "fields": {"year": "year", "month": "month"}, }, } answer_fields = [{"type": "number", "label": "阅读天数"}] def get_answer(self, input: JudgeInput) -> Any: wr = WechatReading(input.apps_init["wechat_reading"]) return wr.month_reading_day_count(self.p.year, self.p.month) class CompareBookLengths(BaseTask): """对比两书字数，将字数更多的一本上架，并在回答中体现该书书名。 判定：① check_on_shelf(字数多者)；② build_answer_checks(书名)。 totalWords 来自 store；采样保证两书字数不等。 注意：objective=hybrid；状态与回答均需通过。 """ templates = [ "对比微信读书里《{book1}》和《{book2}》的字数，告诉我字数多的那本，然后加到书架", "告诉我《{book1}》和《{book2}》在微信读书里哪本更厚，帮我把厚的那本收到书架", ] apps = ["wechat_reading"] scope = "S1" objective = "hybrid" composition = "deep_dive" difficulty = "L2" max_steps = 45 capabilities = ["extract", "reasoning", "create"] parameters = { "book1": { "type": "string", "default": "三体", "description": "书籍1", }, "book2": { "type": "string", "default": "活着", "description": "书籍2", }, "_book_pair": { "sampler": WechatReading.sample_two_books_unequal_word_counts, "fields": {"book1": "book1", "book2": "book2"}, }, } expected_changes = ["shelf", "bookProgress", "readingBookIds", "allProgressBookIds"] answer_fields = [{"type": "choice", "label": "字数更多的书名", "options": ["{book1}", "{book2}"]}] def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]: wr = WechatReading(input.apps["wechat_reading"]) b1 = wr.require_book_by_title(self.p.book1) b2 = wr.require_book_by_title(self.p.book2) w1, w2 = int(b1["totalWords"]), int(b2["totalWords"]) thicker = self.p.book1 if w1 > w2 else self.p.book2 checks = [wr.check_on_shelf(thicker, field="shelf_thicker_book")] checks.extend(build_answer_checks(thicker, input.answer)) return checks class FindHighestRatedBookInCategory(AnswerTask): """验证某分类下评分最高的书名（同分并列时多答案均可）。 判定：get_answer 用 highest_rated_books_in_category；唯一最高返回 str， 并列返回 re.Pattern 匹配任一书名（§4.3）。 """ templates = [ "微信读书{category}分类里评分最高的书是哪本", "看看微信读书{category}类的书哪本评分最高", ] apps = ["wechat_reading"] scope = "S1" objective = "query" composition = "deep_dive" difficulty = "L3" capabilities = ["extract", "reasoning"] parameters = { "category": { "type": "string", "default": "文学", "sampler": WechatReading.sample_category_with_multiple_books, "description": "书籍分类", }, } answer_fields = [{"type": "text", "label": "评分最高的书名", "hint": "如：围城"}] def get_answer(self, input: JudgeInput) -> Any: wr = WechatReading(input.apps_init["wechat_reading"]) best_books = wr.highest_rated_books_in_category(self.p.category) if len(best_books) == 1: return str(best_books[0]["title"]) return re.compile("|".join(re.escape(str(book["title"])) for book in best_books)) def get_expected_response(self, input): wr = WechatReading(input.apps_init["wechat_reading"]) best_books = wr.highest_rated_books_in_category(self.p.category) # In grounded mode, return the first tied book's title return [str(best_books[0]["title"])] class ConfigureReaderSettings(CriteriaTask): """同时设置阅读器字号与系统级翻页方式。 判定：criteria 同时检查 readerPrefs.fontSize 与 settings.pageTurnStyle； _post_sample：_invert_criteria。 """ templates = [ "把微信读书的阅读器字体大小调成{font_size}，翻页方式改成{style}", "在微信读书里把阅读器字体大小改成{font_size}，顺便把翻页方式换成{style}", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "sequential" difficulty = "L2" capabilities = ["settings"] parameters = { "font_size": { "type": "enum", "values": WECHAT_READING_FONT_SIZE_VALUES, "default": 22, }, "style": { "type": "enum", "values": WECHAT_READING_PAGE_TURN_STYLE_VALUES, "default": "仿真翻页", }, } criteria = { "readerPrefs.fontSize": "{font_size}", "settings.pageTurnStyle": "{style}", } async def _post_sample(self, env): await self._invert_criteria(env) class UnfollowUser(BaseTask): """验证是否取消关注指定用户（初始为已关注）。 判定：check_following(user_id, expected=False)。 注意：user_id / user_name 由 sample_following_user 协同采样。 """ templates = [ "在微信读书取消关注{user_name}", "帮我取关微信读书上的{user_name}", "Unfollow {user_name} on WeChat Read", "Stop following {user_name} on WeChat Read", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "sequential" difficulty = "L1" capabilities = ["edit"] parameters = { "user_id": { "type": "string", "default": "user_508", }, "user_name": { "type": "string", "default": "508", }, "_following_user": { "sampler": WechatReading.sample_following_user, "fields": {"user_id": "user_id", "user_name": "user_name"}, }, } expected_changes = ["user.following"] def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]: wr = WechatReading(input.apps["wechat_reading"]) return [wr.check_following(self.p.user_id, expected=False)] class SetProfileVisibility(CriteriaTask): """验证主页可见范围是否为目标枚举（仅自己 / 互关 / 所有人）。 判定：criteria settings.privacy.profile.visibility；_post_sample：_invert_criteria。 """ templates = [ "把微信读书主页可见范围改成{visibility}", "微信读书的主页可见性帮我调成{visibility}", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "sequential" difficulty = "L2" capabilities = ["settings"] parameters = { "visibility": { "type": "enum", "values": WECHAT_READING_PROFILE_VISIBILITY_VALUES, "default": "仅自己可见", }, } criteria = {"settings.privacy.profile.visibility": "{visibility}"} async def _post_sample(self, env): await self._invert_criteria(env) class ReadBookProgress(BaseTask): """验证是否将指定书阅读进度推进到不低于某百分比。 判定：check_progress(book_title, min_pct)；进度由 bookProgress.charOffset 与 store.totalWords 推导。 """ templates = [ "把微信读书里《{book_title}》翻到{percentage}%的位置", "打开微信读书里的《{book_title}》读到{percentage}%左右", "Turn to the {percentage}% position of 《{book_title}》 in WeChat Read", "Read 《{book_title}》 on WeChat Read to about {percentage}%", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "sequential" difficulty = "L3" capabilities = ["edit"] parameters = { "book_title": { "type": "string", "default": "红楼梦", "description": "书籍标题（必须在书架中）", }, "percentage": { "type": "integer", "default": 20, "description": "阅读进度百分比", }, "_progress_target": { "sampler": WechatReading.sample_progress_target, "fields": {"book_title": "book_title", "percentage": "percentage"}, }, } expected_changes = ["bookProgress", "readingBookIds", "allProgressBookIds"] def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]: wr = WechatReading(input.apps["wechat_reading"]) return [wr.check_progress(self.p.book_title, self.p.percentage)] # ============================================================================= # L4 — 复杂组合与条件分支 # ============================================================================= class OrganizeShelfByRecommendation(BaseTask): """按推荐值阈值清理书架：删除推荐值不高于阈值的书，保留更高推荐的书。 判定：一项自定义 check — 当前书架 bookId 集合应等于初始架中 recommendedValue > threshold 的子集（与 check_goals 实现一致）。 注意：依赖 init 与 curr 快照对比，非单一 criteria。 """ templates = [ "整理微信读书书架，把推荐值不高于{recommendation}%的书都删掉", "微信读书书架里推荐值不超过{recommendation}%的书帮我清理掉", "Clean up my WeChat Read bookshelf by removing all books with a recommendation score of {recommendation}% or below", "Organize my WeChat Read bookshelf and delete books with a recommendation value no higher than {recommendation}%", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "deep_dive" difficulty = "L4" capabilities = ["delete", "reasoning"] parameters = { "recommendation": { "type": "float", "default": 95.0, "description": "推荐值阈值（百分比）", }, } expected_changes = ["shelf"] def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]: init_wr = WechatReading(input.apps_init["wechat_reading"]) curr_wr = WechatReading(input.apps["wechat_reading"]) expected_ids = sorted( str(item["bookId"]) for item in init_wr.shelf if float(init_wr.require_store_book(str(item["bookId"]))["recommendedValue"]) > self.p.recommendation ) actual_ids = sorted(str(item["bookId"]) for item in curr_wr.shelf) return [{ "field": "shelf", "expected": expected_ids, "actual": actual_ids, "passed": actual_ids == expected_ids, }] class AddBookAndReadTo(BaseTask): """加书到架并将该书读到不低于目标进度。 判定：① check_on_shelf；② check_progress；两项均须通过。 注意：objective=hybrid；初始书不在架由采样保证。 """ templates = [ "帮我在微信读书找到《{book_title}》加到书架，调整读书进度到{percentage}%", "Find 《{book_title}》 on WeChat Read, add it to my bookshelf, and set the reading progress to {percentage}%", ] apps = ["wechat_reading"] scope = "S1" objective = "hybrid" composition = "sequential" difficulty = "L2" capabilities = ["search", "create", "edit"] parameters = { "book_title": { "type": "string", "default": "三体", "description": "书籍标题（初始不在书架）", }, "percentage": { "type": "integer", "default": 20, "description": "阅读进度百分比", }, "_add_read": { "sampler": WechatReading.sample_add_book_and_read, "fields": {"book_title": "book_title", "percentage": "percentage"}, }, } expected_changes = ["shelf", "bookProgress", "readingBookIds", "allProgressBookIds"] def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]: wr = WechatReading(input.apps["wechat_reading"]) return [ wr.check_on_shelf(self.p.book_title, field="shelf_after_add"), wr.check_progress(self.p.book_title, float(self.p.percentage), field="read_progress"), ] class FindLowestProgressAndRead(BaseTask): """将书架中进度最低的书读到目标进度。 判定：先在 **初始 state** 上用 WechatReading.shelf_book_title_with_lowest_progress() 锁定「起始进度最低」的书名，然后在最终 state 上只检查这本书的进度 >= percentage。并列最低时与 App 内 shelf 遍历顺序一致。 注意：percentage 由 sample_percentage_for_lowest_progress_read 单独采样， 保证相对采样时刻的最低进度书存在可达目标；判定时书名与初始 state 绑定， 避免用户先把最低那本读高后判定目标“跳到下一本”。objective=operate。 """ templates = [ "微信读书书架里哪本书我读的进度最低，帮我翻到{percentage}%的位置", "看看微信读书书架上我进度最落后的是哪本，翻到{percentage}%的位置", "Which book on my WeChat Read bookshelf has the lowest reading progress? Jump to the {percentage}% position for me", "Find the book with the lowest progress on my WeChat Read bookshelf and jump to the {percentage}% position", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "deep_dive" difficulty = "L3" max_steps = 60 capabilities = ["reasoning", "edit"] parameters = { "percentage": { "type": "integer", "default": 50, "description": "当前最低进度书需达到的目标百分比", }, "_lowest_pct": { "sampler": WechatReading.sample_percentage_for_lowest_progress_read, "fields": {"percentage": "percentage"}, }, } expected_changes = [ "bookProgress", "readingBookIds", "allProgressBookIds", "finishedBookIds", "homeFinishedBookIds", ] def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]: init_wr = WechatReading(input.apps_init["wechat_reading"]) curr_wr = WechatReading(input.apps["wechat_reading"]) title = init_wr.shelf_book_title_with_lowest_progress() return [curr_wr.check_progress(title, float(self.p.percentage))] class PrivacyAndThemeBundle(CriteriaTask): """同时设置阅读器颜色（themeColor）、开启一项隐私开关、设置翻页方式。 判定：criteria 三条 — readerPrefs.themeColor、settings.privacy.{setting_key}=True、 settings.pageTurnStyle；隐私项由 sample_privacy_setting_bundle 采样。 _post_sample：_invert_criteria。 注意：与 App 阅读器「主题」面板首行「颜色」一致（readerPrefs.themeColor： white/yellow/green/dark），不是第二行「背景」（themeBg）。展示名白色/米黄/ 绿色/深色的内部值见 WECHAT_READING_THEME_COLOR_PARAM。 """ templates = [ "把微信读书的阅读颜色换成{theme_color}，开启\"{privacy_label}\"，再把翻页方式改成{style}", "在微信读书里把阅读颜色调成{theme_color}、开启\"{privacy_label}\"、翻页方式设成{style}", ] apps = ["wechat_reading"] scope = "S1" objective = "operate" composition = "sequential" difficulty = "L3" capabilities = ["settings"] parameters = { "theme_color": { "type": "enum", "values": WECHAT_READING_THEME_COLOR_PARAM, "default": "yellow", }, "privacy_label": { "type": "string", "default": "关注你须获得你的同意", }, "setting_key": { "type": "string", "default": "requireFollowRequest", }, "style": { "type": "enum", "values": WECHAT_READING_PAGE_TURN_STYLE_VALUES, "default": "仿真翻页", }, "_privacy_setting": { "sampler": WechatReading.sample_privacy_setting_bundle, "fields": {"privacy_label": "privacy_label", "setting_key": "setting_key"}, }, } criteria = { "readerPrefs.themeColor": "{theme_color}", "settings.privacy.{setting_key}": True, "settings.pageTurnStyle": "{style}", } async def _post_sample(self, env): await self._invert_criteria(env)