From 1bcfd47ca2564229941586f7e45874309c4f4b18 Mon Sep 17 00:00:00 2001 From: jindi <1259666097@qq.com> Date: Fri, 17 Jul 2026 17:45:16 +0800 Subject: [PATCH 1/4] feat: sync tools with latest Uni-Parser --- README.md | 31 +- mcp_server/README.md | 2 +- playground/01.quick_start.ipynb | 1366 ++--------------- playground/02.advance.ipynb | 358 +++-- playground/04.use_callbacks.py | 2 +- playground/app.account_information.ipynb | 1153 ++++++++++++++ playground/app.caption_extraction.ipynb | 32 +- playground/app.crop_images.ipynb | 26 +- playground/app.molecule_extracrtion.ipynb | 339 ++-- skills/UniParser-Tools/SKILL.md | 8 +- .../references/api-reference.md | 16 +- .../references/data-classes.md | 2 +- skills/UniParser-Tools/references/notes.md | 5 +- skills/UniParser-Tools/references/patterns.md | 37 +- .../UniParser-Tools/scripts/fetch_by_token.py | 2 +- .../UniParser-Tools/scripts/parse_document.py | 4 +- tests/unit/test_client.py | 95 +- tests/unit/test_constant.py | 4 + tests/unit/test_convert.py | 2 +- tests/unit/test_dataclass_results.py | 8 + uniparser_tools/api/clients.py | 111 +- uniparser_tools/common/constant.py | 6 + uniparser_tools/common/dataclass.py | 2 + 23 files changed, 2030 insertions(+), 1581 deletions(-) create mode 100644 playground/app.account_information.ipynb diff --git a/README.md b/README.md index a61d0c6..0c90032 100644 --- a/README.md +++ b/README.md @@ -131,7 +131,7 @@ from uniparser_tools.common.constant import ParseMode, ParseModeTextual # 科学文献解析模式（推荐默认值） result = parser.trigger_file( - pdf_path="./example.pdf", + file_path="./example.pdf", textual=ParseModeTextual.OCRHighQuality, # high quality equation=ParseMode.OCRHighQuality, # high quality table=ParseMode.OCRHighQuality, # high quality @@ -194,7 +194,7 @@ UniParser 支持在异步任务完成后通过 HTTP POST 回调结果到指定 ```python # 提交带回调地址的异步解析任务 result = parser.trigger_file( - pdf_path="./example.pdf", + file_path="./example.pdf", sync=False, # 必须为 False 才能触发异步回调 callback_url="https://your-server.com/api/callback", callback_secret="your-shared-secret", # 用于校验回调内容的签名 @@ -212,7 +212,7 @@ if result["status"] == "success": print(f"异步任务已提交，完成后将回调到指定地址。Token: {token}") ``` -回调请求的 Payload 将包含 `checksum` 和 `content`。你可以使用 `callback_secret` 对 `content` 进行 HMAC-SHA256 签名校验，以确保内容未被篡改。 +回调结果以 JSON 请求体发送，签名位于 `X-UniParser-Signature: sha256=` 请求头中。接收端必须使用 `callback_secret` 对原始请求体字节计算 HMAC-SHA256，再使用常量时间比较校验签名。 ### 5. 解析图片文件 @@ -232,6 +232,25 @@ if result["status"] == "success": # 使用 token 获取解析结果 ``` +### 6. 解析 URL 或对象存储文件 + +`trigger_url` 支持 `http(s)://`、`s3://`、`oss://`、`tos://` 和指向绝对路径的 `file://` URL；来源可以是 PDF 或常见图片格式。 + +```python +result = parser.trigger_url( + pdf_url="tos://bucket/path/document.pdf", + textual=ParseModeTextual.OCRHighQuality, +) +``` + +### 7. 获取第三方格式输出 + +```python +from uniparser_tools.common.constant import ThirdPartyFormatter + +result = parser.get_third_party_output(token, formatter=ThirdPartyFormatter.MinerU) +``` + ## 使用示例 ### 图文对提取 @@ -326,7 +345,7 @@ token = result["token"] ## 面向 AI Agent -本仓库提供 **Agent Skill**（`skills/UniParser-Tools/`），让 Cursor、Claude Code 等助手在对话中自动完成 PDF / 图片 / 公网 PDF 链接 → 结构化 Markdown 与版面 JSON 的解析。用户只需安装 Skill、配置 API Key，并用自然语言或下方触发词发起任务；具体执行步骤由 Skill 内的 `SKILL.md` 指导 Agent，无需手动敲命令。 +本仓库提供 **Agent Skill**（`skills/UniParser-Tools/`），让 Cursor、Claude Code 等助手在对话中自动完成 PDF / 图片 / 支持的文件 URL → 结构化 Markdown 与版面 JSON 的解析。用户只需安装 Skill、配置 API Key，并用自然语言或下方触发词发起任务；具体执行步骤由 Skill 内的 `SKILL.md` 指导 Agent，无需手动敲命令。 ### 快速使用 Skill @@ -346,7 +365,7 @@ export UNIPARSER_API_KEY="your-api-key" **3. 在 Agent 中使用 Skill** -在 Agent 对话中上传文件、粘贴公网 PDF 链接，或使用类似表述即可触发 Skill，例如： +在 Agent 对话中上传文件、粘贴支持的文件链接，或使用类似表述即可触发 Skill，例如： - 中文：`解析这个 PDF`、`PDF 转 Markdown`、`提取论文`、`文档解析`、`表格提取`、`公式识别`、`化学分子` - 英文：`parse this PDF`、`extract this paper`、`PDF to markdown`、`UniParser`、`scientific paper` @@ -400,7 +419,7 @@ UniParser 提供了基于 [Model Context Protocol](https://modelcontextprotocol. | `uniparser_health` | 检查服务健康状态 | | `uniparser_version` | 获取服务版本信息 | | `uniparser_parse_file` | 解析本机 PDF（传入绝对路径），返回 `content` 文本 | -| `uniparser_parse_url` | 解析公网 PDF URL，返回 `content` 文本 | +| `uniparser_parse_url` | 解析支持的文件 URL，返回 `content` 文本 | ### 快速启动 diff --git a/mcp_server/README.md b/mcp_server/README.md index 65a2069..48c4dd8 100644 --- a/mcp_server/README.md +++ b/mcp_server/README.md @@ -57,7 +57,7 @@ MCP 进程通过 [`UniParserClient`](../uniparser_tools/api/clients.py) 访问 | `uniparser_health` | `GET /health`，返回服务健康状态字符串 | | `uniparser_version` | `GET /version`，返回版本信息 | | `uniparser_parse_file` | 参数：本机 PDF **绝对路径**。流程：`POST /trigger-file-async` → 成功后再 `POST /get-formatted`，返回合并后的 `content` 文本 | -| `uniparser_parse_url` | 参数：公网可访问的 PDF **URL**。流程：`POST /trigger-url-async` → `POST /get-formatted`，返回 `content` | +| `uniparser_parse_url` | 参数：服务端可访问的文件 **URL**（支持 `http(s)`、`s3`、`oss`、`tos`、`file`）。流程：`POST /trigger-url-async` → `POST /get-formatted`，返回 `content` | `uniparser_parse_file` 仅适合**同机**或**体积较小**的 PDF；大文件或带宽受限时，建议将文件放到公网可访问地址后使用 `uniparser_parse_url`。 diff --git a/playground/01.quick_start.ipynb b/playground/01.quick_start.ipynb index a67cc13..c5505aa 100644 --- a/playground/01.quick_start.ipynb +++ b/playground/01.quick_start.ipynb @@ -33,6 +33,18 @@ "from uniparser_tools.utils.convert import dict2obj\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef676b90", + "metadata": {}, + "outputs": [], + "source": [ + "from getpass import getpass\n", + "\n", + "os.environ[\"UNIPARSER_API_KEY\"] = getpass(\"请输入 Uni-Parser API Key: \")" + ] + }, { "cell_type": "markdown", "id": "448421d5", @@ -43,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "3a98b8d3", "metadata": {}, "outputs": [], @@ -83,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "75a2ef72", "metadata": {}, "outputs": [ @@ -91,7 +103,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "41c7700e6fbf5896884ea2f28002a85f\n" + "78f5e7c97e5a5fa3b66962d75f81149d\n" ] } ], @@ -143,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "03cf9226", "metadata": {}, "outputs": [], @@ -217,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "d545817b", "metadata": {}, "outputs": [], @@ -246,56 +258,56 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "f0d53f1d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[{'class': 'documenttitle',\n", - " 'confidence': 0.888671875,\n", - " 'float_xyxy': [0.24928193933823528,\n", - " 0.1336669921875,\n", - " 0.7216222426470589,\n", - " 0.1522216796875],\n", + "[{'class': 'pageheader',\n", + " 'confidence': 0.873046875,\n", + " 'float_xyxy': [0.10816390276166003,\n", + " 0.0209808349609375,\n", + " 0.18095741980271884,\n", + " 0.0582275390625],\n", + " 'page': 0,\n", + " 'str': '\\n'},\n", + " {'class': 'pageheader',\n", + " 'confidence': 0.8916015625,\n", + " 'float_xyxy': [0.2147487704465075,\n", + " 0.02435302734375,\n", + " 0.7850962401912025,\n", + " 0.05181884765625],\n", + " 'page': 0,\n", + " 'str': '\\n'},\n", + " {'class': 'documenttitle',\n", + " 'confidence': 0.88818359375,\n", + " 'float_xyxy': [0.24932963862870247,\n", + " 0.1337890625,\n", + " 0.7225664511494253,\n", + " 0.15234375],\n", " 'page': 0,\n", " 'str': '\\\\begin{documenttitle}\\nDeep Residual Learning for Image Recognition\\n\\\\end{documenttitle}\\n'},\n", " {'class': 'paragraph',\n", - " 'confidence': 0.533203125,\n", - " 'float_xyxy': [0.22179457720588236,\n", - " 0.1917724609375,\n", - " 0.7538488051470589,\n", - " 0.2474365234375],\n", + " 'confidence': 0.552734375,\n", + " 'float_xyxy': [0.221854428292164,\n", + " 0.1915283203125,\n", + " 0.7522523105935013,\n", + " 0.2479248046875],\n", " 'page': 0,\n", " 'str': 'Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com\\n'},\n", " {'class': 'title',\n", - " 'confidence': 0.85595703125,\n", - " 'float_xyxy': [0.23743393841911764,\n", - " 0.284912109375,\n", - " 0.3119973575367647,\n", - " 0.297119140625],\n", - " 'page': 0,\n", - " 'str': '\\\\begin{title}\\nAbstract\\n\\\\end{title}\\n'},\n", - " {'class': 'paragraph',\n", - " 'confidence': 0.97998046875,\n", - " 'float_xyxy': [0.0800924862132353,\n", - " 0.310546875,\n", - " 0.4694967830882353,\n", - " 0.5341796875],\n", + " 'confidence': 0.85791015625,\n", + " 'float_xyxy': [0.2374868755526083,\n", + " 0.28466796875,\n", + " 0.31233313819352343,\n", + " 0.296875],\n", " 'page': 0,\n", - " 'str': 'Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learn- ing residual functions with reference to the layer inputs, in- stead of learning unreferenced functions. We provide com- prehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complex- ity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.\\n'},\n", - " {'class': 'paragraph',\n", - " 'confidence': 0.97705078125,\n", - " 'float_xyxy': [0.07882869944852941,\n", - " 0.537109375,\n", - " 0.4694967830882353,\n", - " 0.6552734375],\n", - " 'page': 0,\n", - " 'str': 'The depth of representations is of central importance for many visual recognition tasks. Solely due to our ex- tremely deep representations, we obtain a 28% relative im- provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local- ization, COCO detection, and COCO segmentation.\\n'}]" + " 'str': '\\\\begin{title}\\nAbstract\\n\\\\end{title}\\n'}]" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "9d5e44a6", "metadata": {}, "outputs": [], @@ -346,1226 +358,106 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "f837f24e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[{'token': '41c7700e6fbf5896884ea2f28002a85f',\n", + "[{'token': '78f5e7c97e5a5fa3b66962d75f81149d',\n", " 'page': 0,\n", - " 'block': 14,\n", - " 'bbox': {'x1': 0.24928193933823528,\n", - " 'y1': 0.1336669921875,\n", - " 'x2': 0.7216222426470589,\n", - " 'y2': 0.1522216796875},\n", - 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" 'Zhang',\n", - " 'Shaoqing',\n", - " 'Ren',\n", - " 'Jian',\n", - " 'Sun',\n", - " 'Microsoft',\n", - " 'Research',\n", - " '{kahe,',\n", - " 'v-xiangz,',\n", - " 'v-shren,',\n", - " 'jiansun}@microsoft.com'],\n", - " 'text': 'Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com'},\n", - " {'token': '41c7700e6fbf5896884ea2f28002a85f',\n", + " 'bboxes': [],\n", + " 'contents': [],\n", + " 'text': 'This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the version available on IEEE Xplore.'},\n", + " {'token': '78f5e7c97e5a5fa3b66962d75f81149d',\n", " 'page': 0,\n", - " 'block': 17,\n", - " 'bbox': {'x1': 0.23743393841911764,\n", - " 'y1': 0.284912109375,\n", - " 'x2': 0.3119973575367647,\n", - " 'y2': 0.297119140625},\n", - " 'conf': 0.85595703125,\n", - " 'page_size': [1224, 1584],\n", - " 'type': 'title',\n", + " 'block': 1049241159,\n", + " 'bbox': {'x1': 0.24932963862870247,\n", + " 'y1': 0.1337890625,\n", + " 'x2': 0.7225664511494253,\n", + " 'y2': 0.15234375},\n", + " 'conf': 0.88818359375,\n", + " 'page_size': [1131, 1463],\n", + " 'type': 'documenttitle',\n", " 'hidden': False,\n", " 'order': 2,\n", " 'lang': 'en',\n", " 'direction': -1,\n", " 'source': '',\n", - " 'bboxes': [{'x1': 0.2385537390615426,\n", - " 'y1': 0.28434749564739187,\n", - " 'x2': 0.31124212227615655,\n", - " 'y2': 0.2994424453889481}],\n", - " 'contents': ['Abstract'],\n", - " 'text': 'Abstract'},\n", - " {'token': '41c7700e6fbf5896884ea2f28002a85f',\n", + " 'bboxes': [],\n", + " 'contents': [],\n", + " 'text': 'Deep Residual Learning for Image Recognition'},\n", + " {'token': '78f5e7c97e5a5fa3b66962d75f81149d',\n", " 'page': 0,\n", - 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" 'to',\n", - " '152',\n", - " 'layers—8×',\n", - " 'deeper',\n", - " 'than',\n", - " 'VGG',\n", - " 'nets',\n", - " '[40]',\n", - " 'but',\n", - " 'still',\n", - " 'having',\n", - " 'lower',\n", - " 'complex-',\n", - " 'ity.',\n", - " 'An',\n", - " 'ensemble',\n", - " 'of',\n", - " 'these',\n", - " 'residual',\n", - " 'nets',\n", - " 'achieves',\n", - " '3.57%',\n", - " 'error',\n", - " 'on',\n", - " 'the',\n", - " 'ImageNet',\n", - " 'test',\n", - " 'set.',\n", - " 'This',\n", - " 'result',\n", - " 'won',\n", - " 'the',\n", - " '1st',\n", - " 'place',\n", - " 'on',\n", - " 'the',\n", - " 'ILSVRC',\n", - " '2015',\n", - " 'classification',\n", - " 'task.',\n", - " 'We',\n", - " 'also',\n", - " 'present',\n", - " 'analysis',\n", - " 'on',\n", - " 'CIFAR-10',\n", - " 'with',\n", - " '100',\n", - " 'and',\n", - " '1000',\n", - " 'layers.'],\n", - " 'text': 'Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learn- ing residual functions with reference to the layer inputs, in- stead of learning unreferenced functions. We provide com- prehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complex- ity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.'},\n", - " {'token': '41c7700e6fbf5896884ea2f28002a85f',\n", + " 'bboxes': [],\n", + " 'contents': [],\n", + " 'text': 'Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com'},\n", + " {'token': '78f5e7c97e5a5fa3b66962d75f81149d',\n", " 'page': 0,\n", - " 'block': 5,\n", - " 'bbox': {'x1': 0.07882869944852941,\n", - " 'y1': 0.537109375,\n", - " 'x2': 0.4694967830882353,\n", - " 'y2': 0.6552734375},\n", - " 'conf': 0.97705078125,\n", - " 'page_size': [1224, 1584],\n", - " 'type': 'paragraph',\n", + " 'block': 2022718,\n", + " 'bbox': {'x1': 0.2374868755526083,\n", + " 'y1': 0.28466796875,\n", + " 'x2': 0.31233313819352343,\n", + " 'y2': 0.296875},\n", + " 'conf': 0.85791015625,\n", + " 'page_size': [1131, 1463],\n", + " 'type': 'title',\n", " 'hidden': False,\n", " 'order': 4,\n", " 'lang': 'en',\n", " 'direction': -1,\n", " 'source': '',\n", - " 'bboxes': [{'x1': 0.1014150233050577,\n", - 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" 'detection',\n", - " 'dataset.',\n", - " 'Deep',\n", - " 'residual',\n", - " 'nets',\n", - " 'are',\n", - " 'foundations',\n", - " 'of',\n", - " 'our',\n", - " 'submissions',\n", - " 'to',\n", - " 'ILSVRC',\n", - " '&',\n", - " 'COCO',\n", - " '2015',\n", - " 'competitions1,',\n", - " 'where',\n", - " 'we',\n", - " 'also',\n", - " 'won',\n", - " 'the',\n", - " '1st',\n", - " 'places',\n", - " 'on',\n", - " 'the',\n", - " 'tasks',\n", - " 'of',\n", - " 'ImageNet',\n", - " 'detection,',\n", - " 'ImageNet',\n", - " 'local-',\n", - " 'ization,',\n", - " 'COCO',\n", - " 'detection,',\n", - " 'and',\n", - " 'COCO',\n", - " 'segmentation.'],\n", - " 'text': 'The depth of representations is of central importance for many visual recognition tasks. Solely due to our ex- tremely deep representations, we obtain a 28% relative im- provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local- ization, COCO detection, and COCO segmentation.'}]" + " 'bboxes': [],\n", + " 'contents': [],\n", + " 'text': 'Abstract'}]" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1576,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "877ba504", "metadata": {}, "outputs": [], @@ -1586,21 +478,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "074956b3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[TextualResult(token='41c7700e6fbf5896884ea2f28002a85f', page=0, block=14, bbox=BBox(x1=0.24928193933823528, y1=0.1336669921875, x2=0.7216222426470589, y2=0.1522216796875), conf=0.888671875, page_size=[1224, 1584], type='documenttitle', hidden=False, order=0, lang='en', 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We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learn- ing residual functions with reference to the layer inputs, in- stead of learning unreferenced functions. We provide com- prehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complex- ity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. 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x2=0.2758745430341733, y2=0.6559830482559975), BBox(x1=0.27994268080767465, y1=0.6434040262241556, x2=0.3251650442484937, y2=0.6559830482559975), BBox(x1=0.32923480264501637, y1=0.6434040262241556, x2=0.4194680008233762, y2=0.6559830482559975)], contents=['The', 'depth', 'of', 'representations', 'is', 'of', 'central', 'importance', 'for', 'many', 'visual', 'recognition', 'tasks.', 'Solely', 'due', 'to', 'our', 'ex-', 'tremely', 'deep', 'representations,', 'we', 'obtain', 'a', '28%', 'relative', 'im-', 'provement', 'on', 'the', 'COCO', 'object', 'detection', 'dataset.', 'Deep', 'residual', 'nets', 'are', 'foundations', 'of', 'our', 'submissions', 'to', 'ILSVRC', '&', 'COCO', '2015', 'competitions1,', 'where', 'we', 'also', 'won', 'the', '1st', 'places', 'on', 'the', 'tasks', 'of', 'ImageNet', 'detection,', 'ImageNet', 'local-', 'ization,', 'COCO', 'detection,', 'and', 'COCO', 'segmentation.'], text='The depth of representations is of central importance for many visual recognition tasks. Solely due to our ex- tremely deep representations, we obtain a 28% relative im- provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local- ization, COCO detection, and COCO segmentation.')]" + "[TextualResult(token='78f5e7c97e5a5fa3b66962d75f81149d', page=0, block=2002267771, bbox=BBox(x1=0.10816390276166003, y1=0.0209808349609375, x2=0.18095741980271884, y2=0.0582275390625), conf=0.873046875, page_size=[1131, 1463], type='pageheader', hidden=False, order=0, lang='en', direction=, source='', bboxes=[], contents=[], text='CvF'),\n", + " TextualResult(token='78f5e7c97e5a5fa3b66962d75f81149d', page=0, block=497335488, bbox=BBox(x1=0.2147487704465075, y1=0.02435302734375, x2=0.7850962401912025, y2=0.05181884765625), conf=0.8916015625, page_size=[1131, 1463], type='pageheader', hidden=False, order=1, lang='en', direction=, source='', bboxes=[], contents=[], text='This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the version available on IEEE Xplore.'),\n", + " TextualResult(token='78f5e7c97e5a5fa3b66962d75f81149d', page=0, block=1049241159, bbox=BBox(x1=0.24932963862870247, y1=0.1337890625, x2=0.7225664511494253, y2=0.15234375), conf=0.88818359375, page_size=[1131, 1463], type='documenttitle', hidden=False, order=2, lang='en', direction=, source='', bboxes=[], contents=[], text='Deep Residual Learning for Image Recognition'),\n", + " TextualResult(token='78f5e7c97e5a5fa3b66962d75f81149d', page=0, block=217191368, bbox=BBox(x1=0.221854428292164, y1=0.1915283203125, x2=0.7522523105935013, y2=0.2479248046875), conf=0.552734375, page_size=[1131, 1463], type='paragraph', hidden=False, order=3, lang='en', direction=, source='', bboxes=[], contents=[], text='Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com'),\n", + " TextualResult(token='78f5e7c97e5a5fa3b66962d75f81149d', page=0, block=2022718, bbox=BBox(x1=0.2374868755526083, y1=0.28466796875, x2=0.31233313819352343, y2=0.296875), conf=0.85791015625, page_size=[1131, 1463], type='title', hidden=False, order=4, lang='en', direction=, source='', bboxes=[], contents=[], text='Abstract')]" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1634,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "8d3c4c1e", "metadata": {}, "outputs": [ @@ -1642,7 +534,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "632927fcb88c5b329c164b607d97d5af\n" + "21b40942c0f95e44bf8ab3ae9e422825\n" ] } ], @@ -1669,7 +561,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "64c8d1fe", "metadata": {}, "outputs": [], @@ -1740,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "0115c016", "metadata": {}, "outputs": [], @@ -1769,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "631d81c5", "metadata": {}, "outputs": [], @@ -1841,7 +733,7 @@ ], "metadata": { "kernelspec": { - "display_name": "py312", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1855,7 +747,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.13" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/playground/02.advance.ipynb b/playground/02.advance.ipynb index e428834..405c28f 100644 --- a/playground/02.advance.ipynb +++ b/playground/02.advance.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "id": "dabd73e6", "metadata": {}, "outputs": [], @@ -42,6 +42,19 @@ "from uniparser_tools.utils.processor import tree_repr\n" ] }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d755bc22", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "os.environ[\"UNIPARSER_API_KEY\"] = getpass(\"请输入 Uni-Parser API Key: \")" + ] + }, { "cell_type": "markdown", "id": "88f859ad", @@ -52,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "0dc28c62", "metadata": {}, "outputs": [], @@ -64,7 +77,9 @@ "host = \"https://uniparser.dp.tech/\" # 官网\n", "\n", "# 替换为你的认证 api key\n", - "api_key = os.getenv('UNIPARSER_API_KEY')\n", + "api_key = os.getenv(\"UNIPARSER_API_KEY\")\n", + "if not api_key:\n", + " raise RuntimeError(\"请先设置 UNIPARSER_API_KEY 环境变量\")\n", "\n", "# 初始化客户端\n", "parser = UniParserClient(host=host, api_key=api_key)\n", @@ -84,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "4b12dbcd", "metadata": {}, "outputs": [ @@ -92,7 +107,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "trigger file success, token is: 41c7700e6fbf5896884ea2f28002a85f\n" + "trigger file success, token is: 78f5e7c97e5a5fa3b66962d75f81149d\n" ] } ], @@ -128,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "f32cd485", "metadata": {}, "outputs": [], @@ -166,11 +181,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "63460a89", "metadata": {}, "outputs": [], "source": [ + "\n", + "\n", "pages_tree = dict2obj(result[\"pages_tree\"]) " ] }, @@ -185,33 +202,33 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "9a88e09a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[TextualResult(token='41c7700e6fbf5896884ea2f28002a85f', page=0, block=14, bbox=BBox(x1=0.24928193933823528, y1=0.1336669921875, x2=0.7216222426470589, y2=0.1522216796875), conf=0.888671875, page_size=[1224, 1584], type='documenttitle', hidden=False, order=0, lang='en', direction=, source='', bboxes=[BBox(x1=0.25073202295241015, y1=0.13367483350965711, x2=0.30150634167241114, y2=0.15178872118092546), BBox(x1=0.30736691344018074, y1=0.13367483350965711, 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We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learn- ing residual functions with reference to the layer inputs, in- stead of learning unreferenced functions. We provide com- prehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complex- ity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. 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Solely due to our ex- tremely deep representations, we obtain a 28% relative im- provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local- ization, COCO detection, and COCO segmentation.'),\n", - " TextualResult(token='41c7700e6fbf5896884ea2f28002a85f', page=0, block=16, bbox=BBox(x1=0.08167221966911764, y1=0.6767578125, x2=0.20836684283088236, y2=0.6884765625), conf=0.86865234375, page_size=[1224, 1584], type='title', hidden=False, order=5, lang='en', direction=, source='', bboxes=[BBox(x1=0.08188241447498595, y1=0.6757390956686, x2=0.09653339510649638, y2=0.6908340454101562), BBox(x1=0.10141686208887038, y1=0.6757390956686, x2=0.20743109509835836, y2=0.6908340454101562)], contents=['1.', 'Introduction'], text='1. 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We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learn- ing residual functions with reference to the layer inputs, in- stead of learning unreferenced functions. We provide com- prehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complex- ity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. 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visual recognition tasks. Solely due to our ex- tremely deep representations, we obtain a 28% relative im- provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local- ization, COCO detection, and COCO segmentation.'),\n", + " TextualResult(token='78f5e7c97e5a5fa3b66962d75f81149d', page=0, block=278141921, bbox=BBox(x1=0.08163611347120911, y1=0.67626953125, x2=0.2084326301392573, y2=0.68896484375), conf=0.86962890625, page_size=[1131, 1463], type='title', hidden=False, order=7, lang='en', direction=, source='', bboxes=[BBox(x1=0.08188241447498595, y1=0.6757390956686, x2=0.09653339510649638, y2=0.6908340454101562), BBox(x1=0.10141686208887038, y1=0.6757390956686, x2=0.20743109509835836, y2=0.6908340454101562)], contents=['1.', 'Introduction'], text='1. 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Let us consider a shallower architecture and its deeper counterpart that adds more layers onto it. There exists a solution by construction to the deeper model: the added layers are identity mapping, and the other layers are copied from the learned shallower model. The existence of this constructed solution indicates that a deeper model should produce no higher training error than its shallower counterpart. But experiments show that our current solvers on hand are unable to find solutions that'),\n", + " TextualResult(token='78f5e7c97e5a5fa3b66962d75f81149d', page=0, block=1960329424, bbox=BBox(x1=0.08084659593280283, y1=0.87841796875, x2=0.4686576107979664, y2=0.89990234375), conf=0.89990234375, page_size=[1131, 1463], type='pagenote', hidden=False, order=19, lang='en', direction=, source='', bboxes=[BBox(x1=0.09962598015280331, y1=0.8783812089399858, x2=0.42533819659862643, y2=0.8897464636600377), BBox(x1=0.4500470628925398, y1=0.8804044627180004, x2=0.46791206309997957, y2=0.8899645564532039), BBox(x1=0.08188202801872702, y1=0.8902493871823706, x2=0.4643687827914369, y2=0.9000275737107403)], contents=['1http://image-net.org/challenges/LSVRC/2015/', 'and', 'http://mscoco.org/dataset/#detections-challenge2015.'], text='1http://image-net.org/challenges/LSVRC/2015/ and http://mscoco.org/dataset/#detections-challenge2015.'),\n", + " TextualResult(token='78f5e7c97e5a5fa3b66962d75f81149d', page=0, block=779087905, bbox=BBox(x1=0.4882376457504421, y1=0.927734375, x2=0.512238978917993, y2=0.9375), conf=0.80810546875, page_size=[1131, 1463], type='pagenumber', hidden=False, order=20, lang='en', direction=, source='', bboxes=[BBox(x1=0.48222361047283496, y1=0.9249027907246291, x2=0.5110294117647058, y2=0.9400618582060842)], contents=['1770'], text='1770')]" ] }, - "execution_count": 6, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -230,13 +247,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "b1661898", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ + "CF \n", + "\n", + "\n", + "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the version available on IEEE Xplore.\n", + "\n", + "\n", "# Deep Residual Learning for Image Recognition\n", "\n", "\n", @@ -260,9 +283,13 @@ "\n", "\n", "\n", - 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)\n", + "| |\n", + "|----|\n", + "\n", + "\n", + "| |\n", + "|----|\n", "\n", - 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"\n", "Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer “plain” networks. The deeper network has higher training error, and thus test error. Similar phenomena on ImageNet is presented in Fig. 4.\n", "\n", @@ -279,12 +306,6 @@ "The degradation (of training accuracy) indicates that not all systems are similarly easy to optimize. Let us consider a shallower architecture and its deeper counterpart that adds more layers onto it. There exists a solution by construction to the deeper model: the added layers are identity mapping, and the other layers are copied from the learned shallower model. The existence of this constructed solution indicates that a deeper model should produce no higher training error than its shallower counterpart. But experiments show that our current solvers on hand are unable to find solutions that\n", "\n", "\n", - "CyF\n", - "\n", - "\n", - "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the version available on IEEE Xplore.\n", - "\n", - "\n", "1http://image-net.org/challenges/LSVRC/2015/ and http://mscoco.org/dataset/#detections-challenge2015.\n", "\n", "\n", @@ -312,17 +333,58 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "8f601804", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "page=0 top_type=group fig_type=chart source_len=10028\n", + "group\n", + "├─ image\n", + "│ ├─ chart\n", + "│ └─ chart\n", + "└─ imagecaption\n", + "\n" + ] + } + ], "source": [ - "img_cap_group = pages_tree[0][8]" + "from uniparser_tools.common.dataclass import ChartResult, FigureResult, GroupedResult\n", + "\n", + "\n", + "def walk(node):\n", + " yield node\n", + " if isinstance(node, GroupedResult):\n", + " for child in node.items:\n", + " yield from walk(child)\n", + "\n", + "\n", + "def find_media(pages, prefer=(\"figure\", \"chart\")):\n", + " \"\"\"在整棵 pages_tree 里找带 source 的 figure/chart，并返回其所在页顶层节点。\"\"\"\n", + " for page in pages:\n", + " for top in page:\n", + " for n in walk(top):\n", + " if isinstance(n, (FigureResult, ChartResult)) and n.source and str(n.type) in prefer:\n", + " return top, n\n", + " return None, None\n", + "\n", + "\n", + "img_cap_group, one_fig = find_media(pages_tree)\n", + "assert one_fig is not None, \"全文档未找到带 source 的 figure/chart（检查 trigger 时 figure=DumpBase64）\"\n", + "print(\n", + " f\"page={one_fig.page} top_type={img_cap_group.type} \"\n", + " f\"fig_type={one_fig.type} source_len={len(one_fig.source)}\"\n", + ")\n", + "if isinstance(img_cap_group, GroupedResult):\n", + " print(tree_repr(img_cap_group))\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "id": "73ff6cf9", "metadata": {}, "outputs": [ @@ -330,29 +392,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "group\n", - "├─ image\n", - "│ ├─ chart\n", - "│ └─ chart\n", - "└─ imagecaption\n", - "\n" + "0 GroupedResult image\n", + "1 TextualResult imagecaption\n" ] } ], "source": [ - "print(tree_repr(img_cap_group))" + "if isinstance(img_cap_group, GroupedResult):\n", + " for i, child in enumerate(img_cap_group.items):\n", + " print(i, type(child).__name__, child.type)\n", + "else:\n", + " print(\"顶层就是 figure/chart，无嵌套 items\")\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "id": "ae292e50", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -363,21 +425,28 @@ } ], "source": [ - "one_fig = img_cap_group.items[0].items[0]\n", - "\n", - "display(HTML(f''))" + "display(HTML(f''))\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "id": "477eb3e0", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "

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\n", + "
\n", + "\n", "Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer “plain” networks. The deeper network has higher training error, and thus test error. Similar phenomena on ImageNet is presented in Fig. 4.
" ], "text/plain": [ @@ -418,16 +507,20 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "id": "c57dda06", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - 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)\n", + "| |\n", + "|----|\n", + "\n", + "\n", + "| |\n", + "|----|\n", "\n", - 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)\n", "\n", "Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer “plain” networks. The deeper network has higher training error, and thus test error. Similar phenomena on ImageNet is presented in Fig. 4.\n" ], @@ -445,16 +538,24 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "id": "84c33584", "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "\\includegraphics[width=0.5\\textwidth]{}\n", + "\\begin{tabular}{l}\n", + " \\\\\n", + "\\end{tabular}\n", + "\n", + "\n", + "\n", + "\\begin{tabular}{l}\n", + " \\\\\n", + "\\end{tabular}\n", + "\n", "\n", - "\\includegraphics[width=0.5\\textwidth]{}\n", "\n", "\\textbf{Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer {\\textquotedblleft}plain{\\textquotedblright} networks. The deeper network has higher training error, and thus test error. Similar phenomena on ImageNet is presented in Fig. 4.}\n" ], @@ -481,17 +582,52 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "id": "43024e4f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "page=2 top_type=group latex='\\\\mathbf {y} = \\\\mathcal {F} (\\\\mathbf {x}, \\\\{W _ {i} \\\\}) + \\\\mathbf {x}.'\n", + "group\n", + "├─ equation\n", + "└─ equationid\n", + "\n" + ] + } + ], "source": [ - "math_group = pages_tree[2][14]" + "from uniparser_tools.common.dataclass import EquationResult, GroupedResult\n", + "\n", + "\n", + "def walk(node):\n", + " yield node\n", + " if isinstance(node, GroupedResult):\n", + " for child in node.items:\n", + " yield from walk(child)\n", + "\n", + "\n", + "def find_equation(pages):\n", + " for page in pages:\n", + " for top in page:\n", + " for n in walk(top):\n", + " if isinstance(n, EquationResult) and n.latex_repr:\n", + " return top, n\n", + " return None, None\n", + "\n", + "\n", + "math_group, one_eq = find_equation(pages_tree)\n", + "assert math_group is not None, \"未找到公式（检查 trigger 时 equation=OCRFast）\"\n", + "print(f\"page={getattr(one_eq, 'page', '?')} top_type={math_group.type} latex={one_eq.latex_repr[:80]!r}\")\n", + "if isinstance(math_group, GroupedResult):\n", + " print(tree_repr(math_group))\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "id": "45287e87", "metadata": {}, "outputs": [ @@ -512,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "id": "712e2911", "metadata": {}, "outputs": [ @@ -520,10 +656,11 @@ "data": { "text/markdown": [ "$$\n", - "\\mathbf{y}=\\mathcal{F}(\\mathbf{x},\\{W_{i}\\})+W_{s}\\mathbf{x}.\n", + "\\mathbf {y} = \\mathcal {F} (\\mathbf {x}, \\{W _ {i} \\}) + \\mathbf {x}.\n", "$$\n", "\n", - "(2)\n" + "\n", + "(1)\n" ], "text/plain": [ "" @@ -547,17 +684,52 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "id": "986b4a3a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "page=4 top_type=group\n", + "group\n", + "├─ table\n", + "└─ tablecaption\n", + "\n" + ] + } + ], "source": [ - "table_group = pages_tree[5][0]" + "from uniparser_tools.common.dataclass import GroupedResult, TabularResult\n", + "\n", + "\n", + "def walk(node):\n", + " yield node\n", + " if isinstance(node, GroupedResult):\n", + " for child in node.items:\n", + " yield from walk(child)\n", + "\n", + "\n", + "def find_table(pages):\n", + " for page in pages:\n", + " for top in page:\n", + " for n in walk(top):\n", + " if isinstance(n, TabularResult):\n", + " return top, n\n", + " return None, None\n", + "\n", + "\n", + "table_group, one_table = find_table(pages_tree)\n", + "assert table_group is not None, \"未找到表格（检查 trigger 时 table=OCRFast）\"\n", + "print(f\"page={getattr(one_table, 'page', '?')} top_type={table_group.type}\")\n", + "if isinstance(table_group, GroupedResult):\n", + " print(tree_repr(table_group))\n" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "id": "e8cf8c7c", "metadata": {}, "outputs": [ @@ -578,27 +750,26 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "id": "f5eaef29", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "| model | top-1 err. | top-5 err. |\n", - "|:---------------|:-------------|:-------------|\n", - "| VGG-16 [40] | 28.07 | 9.33 |\n", - "| GoogLeNet [43] | - | 9.15 |\n", - "| PReLU-net [12] | 24.27 | 7.38 |\n", - "| plain-34 | 28.54 | 10.02 |\n", - "| ResNet-34 A | 25.03 | 7.76 |\n", - "| ResNet-34 B | 24.52 | 7.46 |\n", - "| ResNet-34 C | 24.19 | 7.40 |\n", - "| ResNet-50 | 22.85 | 6.71 |\n", - "| ResNet-101 | 21.75 | 6.05 |\n", - "| ResNet-152 | 21.43 | 5.71 |\n", - "\n", - "Table 3. Error rates (%, 10-crop testing) on ImageNet validation. VGG-16 is based on our test. ResNet-50/101/152 are of option B that only uses projections for increasing dimensions.\n" + "| layer name | output size | 18-layer | | 34-layer | | 50-layer | | 101-layer | | 152-layer | |\n", + "|:-------------|:--------------|:------------------|:------------------|:-------------------|:---------------------------------------------------|:-----------------------|:------------------|:----------------------------------|:------------------|:----------------------------------|:--------|\n", + "| conv1 | 112×112 | 7×7, 64, stride 2 | 7×7, 64, stride 2 | 7×7, 64, stride 2 | 7×7, 64, stride 2 | 7×7, 64, stride 2 | 7×7, 64, stride 2 | 7×7, 64, stride 2 | 7×7, 64, stride 2 | | |\n", + "| conv2_x | 56×56 3×3,64 | 3×3,64 | 3×3,64 | 1×1,64 | 3×3 max pool, stride 2 | 3×3 max pool, stride 2 | 1×1, 64 | | | 1×1,64 | |\n", + "| conv3_x | 28×28 | 3×3,64 3×3,128 | ×2 ×2 | ×3 3×3,64 3×3, 128 | 3×3,64 1×1, 256 1×1, 128 3×3,128 | ×3 | | 3×3,64 1×1, 256 1×1, 128 3×3, 128 | ×3 ×4 | 3×3,64 1×1,256 1×1, 128 | ×3 1d:) |\n", + "| conv4_x | | 3×3,128 3×3,256 | | ×4 3×3,128 3×3,256 | 1×1,512 1×1, 256 | ×4 (cd:) | 1×1, 256 | 1×1,512 | | 3×3,128 1×1, 512 (cid:1) 1×1, 256 | ×8 |\n", + "| | 14×14 | 3×3,256 3×3,512 | ×2 | ×6 3×3,256 3×3,512 | 3×3,256 1×1, 1024 1×1,512 | ×6 L d:) | 1×1, 1024 | 3×3,256 (cd:) 1×1,512 | ×23 | 3×3,256 1×1, 1024 1×1, 512 | ×36 1: |\n", + "| conv5_x | 7×7 1×1 | 3×3,512 | ×2 | ×3 3×3,512 | 3×3,512 1×1, 2048 average pool, 1000-d fc, softmax | ×3 | | 3×3,512 1×1,2048 | ×3 | 3×3,512 1×1,2048 | ×3 |\n", + "| FLOPs | FLOPs | | | | | | | | | | |\n", + "| | | 1.8×109 | | 3.6×109 | 3.8×109 | | 7.6×109 | | 11.3×109 | | |\n", + "\n", + "\n", + "Table 1. Architectures for ImageNet. Building blocks are shown in brackets (see also Fig. 5), with the numbers of blocks stacked. Down- sampling is performed by conv3 1, conv4 1, and conv5 1 with a stride of 2.\n" ], "text/plain": [ "" @@ -622,13 +793,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "id": "dc68c987", "metadata": {}, "outputs": [], "source": [ - "with open(f\"{save_dir}/page_5.md\", \"w\") as f:\n", - " f.write(\"\\n\\n\".join([item.format_as(FormatFlag.Markdown) for item in pages_tree[5]]))" + "page_idx = min(5, len(pages_tree) - 1)\n", + "with open(f\"{save_dir}/page_{page_idx}.md\", \"w\") as f:\n", + " f.write(\"\\n\\n\".join(item.format_as(FormatFlag.Markdown) for item in pages_tree[page_idx]))" ] }, { @@ -642,7 +814,7 @@ ], "metadata": { "kernelspec": { - "display_name": "uniparser-tools", + "display_name": "base", "language": "python", "name": "python3" }, @@ -656,7 +828,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.20" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/playground/04.use_callbacks.py b/playground/04.use_callbacks.py index 676d145..71d591b 100644 --- a/playground/04.use_callbacks.py +++ b/playground/04.use_callbacks.py @@ -54,7 +54,7 @@ def main(): print(f"Token: {token}") print("\nUniParser will now process the file in the background.") print("Once finished, it will POST the result to your callback URL.") - print("The payload will include a 'checksum' for you to verify using your 'callback_secret'.") + print("Verify the raw request body with the X-UniParser-Signature header and callback_secret.") else: print(f"Failed to submit task: {result.get('message')}") if "description" in result: diff --git a/playground/app.account_information.ipynb b/playground/app.account_information.ipynb new file mode 100644 index 0000000..e55c5b1 --- /dev/null +++ b/playground/app.account_information.ipynb @@ -0,0 +1,1153 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b1fc841d", + "metadata": {}, + "source": [ + "本教程介绍 UniParser 账户与计费相关接口的用法，包括账户信息、API Key 管理、用量统计和余额查询。\n", + "\n", + "本文以官网 `https://uniparser.dp.tech/` 服务为例进行演示。\n", + "\n", + "这些接口均通过 `X-API-Key` 认证，用户可访问自己账号的数据：\n", + "- 账户自身信息（`/users/me`）\n", + "- API Key 管理（`/api-keys`）\n", + "- 计费与用量信息（`/billing/usage`、`/billing/usage-records`）\n", + "- 余额信息（`/balance`、`/balance/transactions`）" + ] + }, + { + "cell_type": "markdown", + "id": "534a70fb", + "metadata": {}, + "source": [ + "## 导入依赖" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4dd2f488", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "import requests" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a7ce3087", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "os.environ[\"UNIPARSER_API_KEY\"] = getpass(\"请输入 Uni-Parser API Key: \")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "58503742", + "metadata": {}, + "outputs": [], + "source": [ + "host = \"https://uniparser.dp.tech\" # 官网\n", + "\n", + "api_key = os.getenv(\"UNIPARSER_API_KEY\")\n", + "if not api_key:\n", + " raise RuntimeError(\"请先设置 UNIPARSER_API_KEY 环境变量\")\n", + "\n", + "headers = {\"X-API-Key\": api_key}\n", + "timeout = 30\n", + "\n", + "\n", + "def get(path, **params):\n", + " \"\"\"账户/计费相关接口统一走 GET + X-API-Key\"\"\"\n", + " resp = requests.get(f\"{host}{path}\", headers=headers, params=params or None, timeout=timeout)\n", + " resp.raise_for_status()\n", + " return resp.json()\n", + "\n", + "\n", + "def show(title, data):\n", + " print(f\"\\n{'=' * 60}\\n{title}\\n{'=' * 60}\")\n", + " print(json.dumps(data, indent=4, ensure_ascii=False, default=str))" + ] + }, + { + "cell_type": "markdown", + "id": "939064b8", + "metadata": {}, + "source": [ + "## Step.01 账户自身信息\n", + "> `GET /users/me` 返回当前用户基本信息 + 资料；`PUT /users/me` 更新自己的资料字段（示例见下方注释）。" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5ddf0c28", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "账户信息 (GET /users/me)\n", + "============================================================\n", + "{\n", + " \"username\": \"demo_yaoming\",\n", + " \"email\": \"demo_yaoming@guest.example.com\",\n", + " \"is_active\": true,\n", + " \"is_superuser\": false,\n", + " \"id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"created_at\": \"2026-07-17T03:17:44\",\n", + " \"updated_at\": \"2026-07-17T08:58:57\",\n", + " \"status\": \"active\",\n", + " \"rate_limit\": \"50/minute\",\n", + " \"balance\": \"483.05\",\n", + " \"permissions\": {\n", + " \"chart\": 2,\n", + " \"table\": 2,\n", + " \"figure\": 0,\n", + " \"textual\": 2,\n", + " \"equation\": 2,\n", + " \"molecule\": 1,\n", + " \"pdf_pages\": 6000,\n", + " \"expression\": 1,\n", + " \"file_size_mb\": 300\n", + " },\n", + " \"profile\": null\n", + "}\n" + ] + } + ], + "source": [ + "profile = get(\"/users/me\")\n", + "show(\"账户信息 (GET /users/me)\", profile)\n", + "\n", + "# 更新资料示例（部分字段可选，取消注释后可用）\n", + "# updated_profile = requests.put(\n", + "# f\"{host}/users/me\",\n", + "# headers=headers,\n", + "# json={\"timezone\": \"Asia/Shanghai\", \"language\": \"zh-CN\"},\n", + "# timeout=timeout,\n", + "# ).json()\n", + "# show(\"更新后的账户信息 (PUT /users/me)\", updated_profile)" + ] + }, + { + "cell_type": "markdown", + "id": "331fbc26", + "metadata": {}, + "source": [ + "## Step.02 API Key 管理（仅本人的 key）\n", + "> `GET /api-keys` 列出全部 key（不含明文）；创建、解密和撤销都会改变或暴露敏感信息，因此示例默认不执行。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "304dd970", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "API Key 列表 (GET /api-keys)\n", + "============================================================\n", + "[\n", + " {\n", + " \"id\": \"52b9db67-64c9-4b42-9383-dc0323d835a7\",\n", + " \"name\": \"common\",\n", + " \"key_identifier\": \"common\",\n", + " \"permissions\": [],\n", + " \"status\": \"active\",\n", + " \"expires_at\": \"2036-07-17T03:17:44\",\n", + " \"last_used_at\": null,\n", + " \"created_at\": \"2026-07-17T03:17:44\"\n", + " }\n", + "]\n" + ] + } + ], + "source": [ + "api_keys = get(\"/api-keys\")\n", + "show(\"API Key 列表 (GET /api-keys)\", api_keys)\n", + "\n", + "# 如确需查看现有 Key 的明文，请在确认安全影响后手动调用：\n", + "# key_id = api_keys[0][\"id\"]\n", + "# decrypted = get(f\"/api-keys/{key_id}/decrypt\")\n", + "\n", + "# 创建新 key 示例（取消注释后可用，raw_key 仅在响应中返回一次）\n", + "# new_key = requests.post(\n", + "# f\"{host}/api-keys\",\n", + "# headers=headers,\n", + "# json={\"key_identifier\": \"my_script_key\", \"permissions\": []},\n", + "# timeout=timeout,\n", + "# ).json()\n", + "# show(\"新建 Key (POST /api-keys)\", new_key)\n", + "\n", + "# 撤销 key 示例（取消注释后可用）\n", + "# requests.delete(f\"{host}/api-keys/{key_id}\", headers=headers, timeout=timeout)" + ] + }, + { + "cell_type": "markdown", + "id": "d67d20a9", + "metadata": {}, + "source": [ + "## Step.03 计费与用量信息（仅本人数据）\n", + "> `GET /billing/usage` 按周期（`current_month` / `last_month` / 默认近 30 天）汇总请求数、费用、Token、页数；`GET /billing/usage-records` 分页返回最近 2 周的逐条用量明细。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "35e8e8f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "用量汇总 (GET /billing/usage?period=current_month)\n", + "============================================================\n", + "{\n", + " \"total_requests\": 23,\n", + " \"total_cost\": \"26.95\",\n", + " \"total_tokens\": 0,\n", + " \"total_pages\": 0,\n", + " \"usage_by_service\": {\n", + " \"parser\": {\n", + " \"requests\": 23,\n", + " \"cost\": 26.949999999999992,\n", + " \"tokens\": 0,\n", + " \"pages\": 0\n", + " }\n", + " },\n", + " \"usage_by_date\": {\n", + " \"2026-07-17\": {\n", + " \"requests\": 23,\n", + " \"cost\": 26.949999999999992,\n", + " \"tokens\": 0,\n", + " \"pages\": 0\n", + " }\n", + " },\n", + " \"period_start\": \"2026-07-01T00:00:00\",\n", + " \"period_end\": \"2026-07-31T23:59:59.999999\"\n", + "}\n" + ] + } + ], + "source": [ + "period = \"current_month\" # current_month / last_month / 其他值默认近 30 天\n", + "usage_summary = get(\"/billing/usage\", period=period)\n", + "show(f\"用量汇总 (GET /billing/usage?period={period})\", usage_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bf0e19aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "用量明细 (GET /billing/usage-records, 共 23 条，仅近 2 周)\n", + "============================================================\n", + "{\n", + " \"items\": [\n", + " {\n", + " \"request_id\": \"8f8ff1e6-c573-4ba6-8945-54ed16d5e83d\",\n", + " \"endpoint\": \"/trigger-file-async\",\n", + " \"method\": \"POST\",\n", + " \"service_type\": \"parser\",\n", + " \"resource_usage\": {\n", + " \"md5\": \"a349fad16277a338a822f777f22b630b\",\n", + " \"page\": 9.0,\n", + " \"token\": \"78f5e7c97e5a5fa3b66962d75f81149d\",\n", + " \"dict_cfg\": \"{'chart': -1, 'equation': 1, 'expression': -1, 'figure': -1, 'molecule': 1, 'table': 1, 'textual': 3}\",\n", + " \"proc_mol\": 0.0,\n", + " \"proc_equa\": 2.0,\n", + " \"proc_expr\": 0.0,\n", + " \"proc_page\": 9.0,\n", + " \"dict_model\": \"{'admin_debug': False, 'inplace_update': False, 'lang': 'unknown', 'ordering_method': 'gap_tree', 'padding_snip': True, 'pages': None, 'table_cls': False, 'timeout': 540}\",\n", + " \"proc_chart\": 9.0,\n", + " \"proc_table\": 9.0,\n", + " \"proc_figure\": 4.0,\n", + " \"proc_textual\": 169.0,\n", + " \"request_start_time\": \"2026-07-17T08:58:55.296017\"\n", + " },\n", + " \"cost_details\": {\n", + " \"total_cost\": 0.45,\n", + " \"base_cost\": {\n", + " \"amount\": 0.0,\n", + " \"description\": \"基础服务费用 (parser)\",\n", + " \"details\": {\n", + " \"service_type\": \"parser\",\n", + " \"pricing_config\": {\n", + " \"base_cost_per_mb\": 0.005,\n", + " \"base_cost_per_page\": 0.01,\n", + " \"processing_time_cost\": 0.001\n", + " }\n", + " }\n", + " },\n", + " \"resource_costs\": {\n", + " \"amount\": 0.0,\n", + " \"description\": \"资源使用费用\",\n", + " \"details\": {\n", + " \"tokens_used\": 0,\n", + " \"file_size_mb\": 0,\n", + " \"pages_processed\": 0,\n", + " \"processing_time\": 0\n", + " }\n", + " },\n", + " \"additional_costs\": {\n", + " \"amount\": 0.0,\n", + " \"description\": \"额外费用\",\n", + " \"details\": {\n", + " \"processing_cost\": 0.45\n", + " }\n", + " }\n", + " },\n", + " \"total_cost\": \"0.45\",\n", + " \"status\": \"success\",\n", + " \"ip_address\": \"39.106.237.255\",\n", + " \"user_agent\": \"python-requests/2.32.4\",\n", + " \"id\": \"b4d4351c-706a-4133-9755-758d47aae4e5\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"organization_id\": null,\n", + " \"api_key_id\": \"52b9db67-64c9-4b42-9383-dc0323d835a7\",\n", + " \"created_at\": \"2026-07-17T08:58:55\",\n", + " \"completed_at\": \"2026-07-17T08:58:57\"\n", + " },\n", + " {\n", + " \"request_id\": \"efa99caf-1c42-4ec0-8b3a-9b47c0272fc8\",\n", + " \"endpoint\": \"/trigger-url-async\",\n", + " \"method\": \"POST\",\n", + " \"service_type\": \"parser\",\n", + " \"resource_usage\": {\n", + " \"md5\": \"a349fad16277a338a822f777f22b630b\",\n", + " \"page\": 9.0,\n", + " \"token\": \"7c766afc569d54a29c1e1dbc9a6559e7\",\n", + " \"dict_cfg\": \"{'chart': -1, 'equation': 2, 'expression': -1, 'figure': -1, 'molecule': 1, 'table': 2, 'textual': 2}\",\n", + " \"proc_mol\": 0.0,\n", + " \"proc_equa\": 2.0,\n", + " \"proc_expr\": 0.0,\n", + " \"proc_page\": 9.0,\n", + " \"dict_model\": \"{'admin_debug': False, 'inplace_update': False, 'lang': 'unknown', 'ordering_method': 'gap_tree', 'padding_snip': False, 'pages': None, 'table_cls': False, 'timeout': 540}\",\n", + " \"proc_chart\": 9.0,\n", + " \"proc_table\": 9.0,\n", + " \"proc_figure\": 4.0,\n", + " \"proc_textual\": 169.0,\n", + " \"request_start_time\": \"2026-07-17T08:54:20.841800\"\n", + " },\n", + " \"cost_details\": {\n", + " \"total_cost\": 0.45,\n", + " \"base_cost\": {\n", + " \"amount\": 0.0,\n", + " \"description\": \"基础服务费用 (parser)\",\n", + " \"details\": {\n", + " \"service_type\": \"parser\",\n", + " \"pricing_config\": {\n", + " \"base_cost_per_mb\": 0.005,\n", + " \"base_cost_per_page\": 0.01,\n", + " \"processing_time_cost\": 0.001\n", + " }\n", + " }\n", + " },\n", + " \"resource_costs\": {\n", + " \"amount\": 0.0,\n", + " \"description\": \"资源使用费用\",\n", + " \"details\": {\n", + " \"tokens_used\": 0,\n", + " \"file_size_mb\": 0,\n", + " \"pages_processed\": 0,\n", + " \"processing_time\": 0\n", + " }\n", + " },\n", + " \"additional_costs\": {\n", + " 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\"额外费用\",\n", + " \"details\": {\n", + " \"processing_cost\": 1.7000000000000002\n", + " }\n", + " }\n", + " },\n", + " \"total_cost\": \"1.70\",\n", + " \"status\": \"success\",\n", + " \"ip_address\": \"123.57.117.177\",\n", + " \"user_agent\": \"python-requests/2.32.4\",\n", + " \"id\": \"26b1ddf8-d3ff-43e3-8441-a33d0a13a11c\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"organization_id\": null,\n", + " \"api_key_id\": \"52b9db67-64c9-4b42-9383-dc0323d835a7\",\n", + " \"created_at\": \"2026-07-17T05:54:18\",\n", + " \"completed_at\": \"2026-07-17T05:54:20\"\n", + " },\n", + " {\n", + " \"request_id\": \"6e044860-ecdb-4241-950a-1f61aba96183\",\n", + " \"endpoint\": \"/trigger-file-async\",\n", + " \"method\": \"POST\",\n", + " \"service_type\": \"parser\",\n", + " \"resource_usage\": {\n", + " \"md5\": \"6a95094becf27033a77dac83ce454bd0\",\n", + " \"page\": 19.0,\n", + " \"token\": \"2ff6db102b6d527bb4deef6e5cec442a\",\n", + " \"dict_cfg\": \"{'chart': 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" \"resource_costs\": {\n", + " \"amount\": 0.0,\n", + " \"description\": \"资源使用费用\",\n", + " \"details\": {\n", + " \"tokens_used\": 0,\n", + " \"file_size_mb\": 0,\n", + " \"pages_processed\": 0,\n", + " \"processing_time\": 0\n", + " }\n", + " },\n", + " \"additional_costs\": {\n", + " \"amount\": 0.0,\n", + " \"description\": \"额外费用\",\n", + " \"details\": {\n", + " \"processing_cost\": 1.0\n", + " }\n", + " }\n", + " },\n", + " \"total_cost\": \"1.00\",\n", + " \"status\": \"success\",\n", + " \"ip_address\": \"123.57.117.183\",\n", + " \"user_agent\": \"python-requests/2.32.4\",\n", + " \"id\": \"e14bf3cd-ca27-49ec-9578-57078aa58feb\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"organization_id\": null,\n", + " \"api_key_id\": \"52b9db67-64c9-4b42-9383-dc0323d835a7\",\n", + " \"created_at\": \"2026-07-17T04:09:15\",\n", + " \"completed_at\": \"2026-07-17T04:09:22\"\n", + " },\n", + " {\n", + " \"request_id\": \"b4a790ea-5c19-4101-a792-7bee0a22b891\",\n", + " \"endpoint\": \"/trigger-file-async\",\n", + " \"method\": \"POST\",\n", + " \"service_type\": \"parser\",\n", + " \"resource_usage\": {\n", + " \"md5\": \"a349fad16277a338a822f777f22b630b\",\n", + " \"page\": 9.0,\n", + " \"token\": \"78f5e7c97e5a5fa3b66962d75f81149d\",\n", + " \"dict_cfg\": \"{'chart': -1, 'equation': 1, 'expression': -1, 'figure': -1, 'molecule': 1, 'table': 1, 'textual': 3}\",\n", + " \"proc_mol\": 0.0,\n", + " \"proc_equa\": 2.0,\n", + " \"proc_expr\": 0.0,\n", + " \"proc_page\": 9.0,\n", + " \"dict_model\": \"{'admin_debug': False, 'inplace_update': False, 'lang': 'unknown', 'ordering_method': 'gap_tree', 'padding_snip': True, 'pages': None, 'table_cls': False, 'timeout': 540}\",\n", + " \"proc_chart\": 9.0,\n", + " \"proc_table\": 9.0,\n", + " \"proc_figure\": 4.0,\n", + " \"proc_textual\": 169.0,\n", + " \"request_start_time\": \"2026-07-17T04:08:21.906929\"\n", + " },\n", + " \"cost_details\": {\n", + " \"total_cost\": 0.45,\n", + " \"base_cost\": {\n", + " \"amount\": 0.0,\n", + " \"description\": \"基础服务费用 (parser)\",\n", + " \"details\": {\n", + " \"service_type\": \"parser\",\n", + " \"pricing_config\": {\n", + " \"base_cost_per_mb\": 0.005,\n", + " \"base_cost_per_page\": 0.01,\n", + " \"processing_time_cost\": 0.001\n", + " }\n", + " }\n", + " },\n", + " \"resource_costs\": {\n", + " \"amount\": 0.0,\n", + " \"description\": \"资源使用费用\",\n", + " \"details\": {\n", + " \"tokens_used\": 0,\n", + " \"file_size_mb\": 0,\n", + " \"pages_processed\": 0,\n", + " \"processing_time\": 0\n", + " }\n", + " },\n", + " \"additional_costs\": {\n", + " \"amount\": 0.0,\n", + " \"description\": \"额外费用\",\n", + " \"details\": {\n", + " \"processing_cost\": 0.45\n", + " }\n", + " }\n", + " },\n", + " \"total_cost\": \"0.45\",\n", + " \"status\": \"success\",\n", + " \"ip_address\": \"39.96.119.214\",\n", + " \"user_agent\": \"python-requests/2.32.4\",\n", + " \"id\": \"d691ee42-b023-4c85-81e4-a57d76e4345c\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"organization_id\": null,\n", + " \"api_key_id\": \"52b9db67-64c9-4b42-9383-dc0323d835a7\",\n", + " \"created_at\": \"2026-07-17T04:08:22\",\n", + " \"completed_at\": \"2026-07-17T04:08:24\"\n", + " }\n", + " ],\n", + " \"total\": 23,\n", + " \"page\": 1,\n", + " \"size\": 10,\n", + " \"pages\": 3\n", + "}\n" + ] + } + ], + "source": [ + "page, size = 1, 10\n", + "usage_records = get(\"/billing/usage-records\", page=page, size=size)\n", + "show(f\"用量明细 (GET /billing/usage-records, 共 {usage_records.get('total', 0)} 条，仅近 2 周)\", usage_records)" + ] + }, + { + "cell_type": "markdown", + "id": "8e756f8c", + "metadata": {}, + "source": [ + "## Step.04 余额信息（仅本人数据）\n", + "> `GET /balance` 返回当前余额、货币、账户状态等；`GET /balance/transactions` 分页返回余额流水。自助充值 `POST /balance/top-up` 已禁用（返回 410），需联系管理员。" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "498fdae2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "余额 (GET /balance)\n", + "============================================================\n", + "{\n", + " \"balance\": \"483.05\",\n", + " \"currency\": \"CNY\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"username\": \"demo_yaoming\",\n", + " \"status\": \"active\",\n", + " \"bohrium_user_id\": null,\n", + " \"permissions\": {\n", + " \"chart\": 2,\n", + " \"table\": 2,\n", + " \"figure\": 0,\n", + " \"textual\": 2,\n", + " \"equation\": 2,\n", + " \"molecule\": 1,\n", + " \"pdf_pages\": 6000,\n", + " \"expression\": 1,\n", + " \"file_size_mb\": 300\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "balance = get(\"/balance\")\n", + "show(\"余额 (GET /balance)\", balance)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "353a858c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "余额流水 (GET /balance/transactions, 共 24 条)\n", + "============================================================\n", + "{\n", + " \"items\": [\n", + " {\n", + " \"transaction_id\": \"e90d44a7-9d92-46e8-a689-d045604dc237\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"admin_id\": null,\n", + " \"amount\": \"-0.45\",\n", + " \"balance_before\": \"483.50\",\n", + " \"balance_after\": \"483.05\",\n", + " \"transaction_type\": \"usage_deduction\",\n", + " \"description\": \"API 使用扣费\",\n", + " \"reference_id\": \"b4d4351c-706a-4133-9755-758d47aae4e5\",\n", + " \"created_at\": \"2026-07-17T08:58:57\"\n", + " },\n", + " {\n", + " \"transaction_id\": \"3b3b8408-94b3-4b6a-84be-6bc0ffe04d37\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"admin_id\": null,\n", + " \"amount\": \"-0.45\",\n", + " \"balance_before\": \"483.95\",\n", + " \"balance_after\": \"483.50\",\n", + " \"transaction_type\": \"usage_deduction\",\n", + " \"description\": \"API 使用扣费\",\n", + " \"reference_id\": \"f939b76e-e402-4458-bc0b-87b5b2c26e52\",\n", + " \"created_at\": \"2026-07-17T08:54:25\"\n", + " },\n", + " {\n", + " \"transaction_id\": \"6d975bd6-1352-424c-a1d3-81abe4ec6d7a\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"admin_id\": null,\n", + " \"amount\": \"-0.05\",\n", + " \"balance_before\": \"484.00\",\n", + " \"balance_after\": \"483.95\",\n", + " \"transaction_type\": \"usage_deduction\",\n", + " \"description\": \"API 使用扣费\",\n", + " \"reference_id\": \"da93e0ad-834f-4696-868d-c28e1b9fad26\",\n", + " \"created_at\": \"2026-07-17T08:54:13\"\n", + " },\n", + " {\n", + " \"transaction_id\": \"d9a55804-aec5-46e3-8c19-27ab34d8a7db\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"admin_id\": null,\n", + " \"amount\": \"-0.45\",\n", + " \"balance_before\": \"484.45\",\n", + " \"balance_after\": \"484.00\",\n", + " \"transaction_type\": \"usage_deduction\",\n", + " \"description\": \"API 使用扣费\",\n", + " \"reference_id\": \"3cd3a9da-221c-4485-9004-688089173b46\",\n", + " \"created_at\": \"2026-07-17T08:53:56\"\n", + " },\n", + " {\n", + " \"transaction_id\": \"c15d2a8f-57c0-4544-980d-658f8dfa44be\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"admin_id\": null,\n", + " \"amount\": \"-1.00\",\n", + " \"balance_before\": \"485.45\",\n", + " \"balance_after\": \"484.45\",\n", + " \"transaction_type\": \"usage_deduction\",\n", + " \"description\": \"API 使用扣费\",\n", + " \"reference_id\": \"55ec5e45-5894-42e8-a3bd-92a75ce9f874\",\n", + " \"created_at\": \"2026-07-17T05:58:41\"\n", + " },\n", + " {\n", + " \"transaction_id\": \"dd80057b-ca8a-47b9-9678-2ccc35840226\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"admin_id\": null,\n", + " \"amount\": \"-1.70\",\n", + " \"balance_before\": \"487.15\",\n", + " \"balance_after\": \"485.45\",\n", + " \"transaction_type\": \"usage_deduction\",\n", + " \"description\": \"API 使用扣费\",\n", + " \"reference_id\": \"6207ac03-e874-416a-81a2-727db97d40bd\",\n", + " \"created_at\": \"2026-07-17T05:58:14\"\n", + " },\n", + " {\n", + " \"transaction_id\": \"aee16168-88f9-4cd5-a295-0d4aeeb690cb\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"admin_id\": null,\n", + " \"amount\": \"-1.00\",\n", + " \"balance_before\": \"488.15\",\n", + " \"balance_after\": \"487.15\",\n", + " \"transaction_type\": \"usage_deduction\",\n", + " \"description\": \"API 使用扣费\",\n", + " \"reference_id\": \"43491238-138a-42c9-b968-b8e461f84082\",\n", + " \"created_at\": \"2026-07-17T05:55:39\"\n", + " },\n", + " {\n", + " \"transaction_id\": \"7dba1da7-5763-4f3e-9c61-8bd1f6e92b31\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"admin_id\": null,\n", + " \"amount\": \"-1.70\",\n", + " \"balance_before\": \"489.85\",\n", + " \"balance_after\": \"488.15\",\n", + " \"transaction_type\": \"usage_deduction\",\n", + " \"description\": \"API 使用扣费\",\n", + " \"reference_id\": \"26b1ddf8-d3ff-43e3-8441-a33d0a13a11c\",\n", + " \"created_at\": \"2026-07-17T05:54:20\"\n", + " },\n", + " {\n", + " \"transaction_id\": \"3dea591e-5583-49fd-a547-f46b1ea2a95a\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"admin_id\": null,\n", + " \"amount\": \"-1.00\",\n", + " \"balance_before\": \"490.85\",\n", + " \"balance_after\": \"489.85\",\n", + " \"transaction_type\": \"usage_deduction\",\n", + " \"description\": \"API 使用扣费\",\n", + " \"reference_id\": \"e14bf3cd-ca27-49ec-9578-57078aa58feb\",\n", + " \"created_at\": \"2026-07-17T04:09:22\"\n", + " },\n", + " {\n", + " \"transaction_id\": \"6b18f95d-ec80-46f0-91e7-4225c3fd3be8\",\n", + " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", + " \"admin_id\": null,\n", + " \"amount\": \"-0.45\",\n", + " \"balance_before\": \"491.30\",\n", + " \"balance_after\": \"490.85\",\n", + " \"transaction_type\": \"usage_deduction\",\n", + " \"description\": \"API 使用扣费\",\n", + " \"reference_id\": \"d691ee42-b023-4c85-81e4-a57d76e4345c\",\n", + " \"created_at\": \"2026-07-17T04:08:24\"\n", + " }\n", + " ],\n", + " \"total\": 24,\n", + " \"page\": 1,\n", + " \"size\": 10,\n", + " \"pages\": 3\n", + "}\n" + ] + } + ], + "source": [ + "page, size = 1, 10\n", + "txns = get(\"/balance/transactions\", page=page, size=size)\n", + "show(f\"余额流水 (GET /balance/transactions, 共 {txns.get('total', 0)} 条)\", txns)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/playground/app.caption_extraction.ipynb b/playground/app.caption_extraction.ipynb index 837fb34..9b2930c 100644 --- a/playground/app.caption_extraction.ipynb +++ b/playground/app.caption_extraction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "9d663a75", "metadata": {}, "outputs": [], @@ -16,9 +16,27 @@ "from uniparser_tools.tools.caption_extraction.main import Dict, ImageWithCaption, main\n", "\n", "\n", - "###### 以下为示例代码，请自行修改\n", "\n", - "# ==============================================================================================\n", + "from getpass import getpass\n", + "\n", + "os.environ[\"UNIPARSER_API_KEY\"] = getpass(\"请输入 Uni-Parser API Key: \")\n" + ] + }, + { + "cell_type": "markdown", + "id": "01543c94", + "metadata": {}, + "source": [ + "## 以下为示例代码，请自行修改" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eb9cdb05", + "metadata": {}, + "outputs": [], + "source": [ "host = \"https://uniparser.dp.tech/\" # 官网\n", "\n", "# 替换为你的认证 api key\n", @@ -76,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "88dd0a33", "metadata": {}, "outputs": [], @@ -106,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "97c93f9c", "metadata": {}, "outputs": [], @@ -178,7 +196,7 @@ ], "metadata": { "kernelspec": { - "display_name": "uniparser-tools", + "display_name": "base", "language": "python", "name": "python3" }, @@ -192,7 +210,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.20" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/playground/app.crop_images.ipynb b/playground/app.crop_images.ipynb index de290ea..df24ec5 100644 --- a/playground/app.crop_images.ipynb +++ b/playground/app.crop_images.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -38,6 +38,17 @@ "from uniparser_tools.common.constant import FormatFlag, ParseMode, ParseModeTextual" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from getpass import getpass\n", + "\n", + "os.environ[\"UNIPARSER_API_KEY\"] = getpass(\"请输入 Uni-Parser API Key: \")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -47,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -61,8 +72,9 @@ "source": [ "host = \"https://uniparser.dp.tech/\" # 官网\n", "\n", - "# 替换为你的认证 api key\n", - "api_key = \"xxxxxxx\"\n", + "api_key = os.getenv(\"UNIPARSER_API_KEY\")\n", + "if not api_key:\n", + " raise RuntimeError(\"请先设置 UNIPARSER_API_KEY 环境变量\")\n", "# 初始化客户端\n", "parser = UniParserClient(host=host, api_key=api_key)\n", "\n", @@ -89,7 +101,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务提交成功, Token: df84e1c227415776be33b7e0c97c9baa\n" + "任务提交成功, Token: 78f5e7c97e5a5fa3b66962d75f81149d\n" ] } ], @@ -177,7 +189,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "全文共找到 222 个具有边界框的结构化元素\n" + "全文共找到 220 个具有边界框的结构化元素\n" ] } ], @@ -215,7 +227,7 @@ "📊 PDF 图片类型元素统计:\n", " - chart : 9 个\n", " - table : 9 个\n", - " - image : 7 个\n", + " - image : 5 个\n", " - figure : 4 个\n", " - equation : 2 个\n" ] diff --git a/playground/app.molecule_extracrtion.ipynb b/playground/app.molecule_extracrtion.ipynb index 4151be5..9f67bdd 100644 --- a/playground/app.molecule_extracrtion.ipynb +++ b/playground/app.molecule_extracrtion.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "3a68e1a2", "metadata": {}, "outputs": [], @@ -22,15 +22,20 @@ "from uniparser_tools.utils.convert import dict2obj\n", "from uniparser_tools.utils.log import get_root_logger\n", "from uniparser_tools.utils.processor import tree_repr\n", + "from getpass import getpass\n", + "\n", "\n", "\n", "###### 以下为示例代码，请自行修改\n", "\n", "# ==============================================================================================\n", + "os.environ[\"UNIPARSER_API_KEY\"] = getpass(\"请输入 Uni-Parser API Key: \")\n", "host = \"https://uniparser.dp.tech/\" # 官网\n", "\n", "# 替换为你的认证api key\n", - "api_key = os.getenv('UNIPARSER_API_KEY')\n", + "api_key = os.getenv(\"UNIPARSER_API_KEY\")\n", + "if not api_key:\n", + " raise RuntimeError(\"请先设置 UNIPARSER_API_KEY 环境变量\")\n", "\n", "# 初始化客户端\n", "parser = UniParserClient(host=host, api_key=api_key)\n", @@ -44,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "330fb53d", "metadata": {}, "outputs": [ @@ -76,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "d5bec1f0", "metadata": {}, "outputs": [], @@ -90,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "319b7daa", "metadata": {}, "outputs": [], @@ -100,28 +105,28 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "2fbbbe31", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[GroupedResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=2087102168, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, x2=0.7110178715040704, y2=0.17773435687792272), conf=0.96435546875, page_size=[1190, 1684], type='image', hidden=False, order=0, lang='ko', direction=, source='', level=1, method='default', items=[GroupedResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1317312281, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, x2=0.7110178715040704, y2=0.17773435687792272), conf=0.9677734375, page_size=[1190, 1684], type='moleculegroup', hidden=False, order=1, lang='ko', direction=, source='', level=1, method='default', items=[MoleculeResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1379245026, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, 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caption='*C(=O)NC(*)CNC(*)C(=O)O*0:R[3]5:R[2]9:R[1]13:SG[1]', markush=True, smi='*C(=O)NC(*)CNC(*)C(=O)O*', sru=False, drawing=\" \"), TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1499092466, bbox=BBox(x1=0.6968527914095326, y1=0.45214840125584543, x2=0.7276013799074317, y2=0.46777340125584543), conf=0.84716796875, page_size=[1190, 1684], type='moleculeid', hidden=False, order=13, lang='ko', direction=, source='', bboxes=[], contents=[], text='(IV)')])]),\n", - " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=2042119221, bbox=BBox(x1=0.12195787189387473, y1=0.5332030887558454, x2=0.9120928596047794, y2=0.6064452762558454), conf=0.9501953125, page_size=[1190, 1684], type='paragraph', hidden=False, order=14, lang='ko', direction=, source='', bboxes=[], contents=[], text='并与一种酰化剂 $ R^{4}COCl $ （V）反应并去除保护基 SG1，从而得到如权利要求 1 中所述的通式 I 的化合物，其中取代基 $ R^{1} $ 、 $ R^{2} $ 、 $ R^{3} $ 和 $ R^{4} $ 具有上述含义且 $ SG_{1} $ 是一种保护基。'),\n", - " 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dbXWpfhn8Pf2TP8Agqr/AMHPv7V6ftTfEPT7vwv+ztJ47Pgi++I2o6jaaT8L/gf4Ctory/l8K/CjwtqNxb6p4vuLe30iLw7faz4W0DWJL/xnJaal8QL2O7fUryP+17/gl/8A8G4f7C//AATU1nxL44+xn9qH4qapruh6x4U+I3xy8GeEtS1H4aR+G57HUtFHgLSI7W80jw7r1jr9pJrMXjLS7ez8TiSdLM6h9ks7WOMA/lt8E/8ABE3/AIL8/wDBX0fCbxh/wUQ+PGt+B/gVYfEvUryTwx8b/Gmqap8R/BXhPxNb+H9V1zxj8OPhdpmlT+HdQ07U9Oaw0/QLLVvHejXRk0ae2ufsAtUluv7Ev2N/+CC//BMD9iIeDdW+F37N3hvxX8R/Amv6n4l8NfGL4qx2vjb4oadqWq20lpNHB4hlsrCEafa2txdWum2b6e4tLe5mj82UsHH7F0UARQQQWsENtawxW1tbxRwW9vBGkMEEESCOKGGKNVjiiiRVSONFVERQqgAAV8pftK/sI/sc/tjW2gWn7UP7OPwp+N0PhfUb7VtAHjvwxaanLpupalaW1jfXkNwnkXDz3NnZ2ttI00sv7qCNFChRX1lRQB/nQf8ABRH/AINhf2vf2Fvip4Z/a3/4I+eOviR8RNTtfiVqmo6H8NvCq2vhn4wfBG1vEt5/DNx4V8TSa02nePdHtHXWYNd1LUIfCU2kqNDhtdI1cXc8lh2X/BLX/g5d+PH7D3xD+JH7G3/Baux+M+u6/wCFvFFxpul/FHWdOtNe+K/gTxbN4kttL1bwt8R7PUNU0m01fwPZQXOoapbeLdH1a6k0Wz0SOw07Rdei1M31p/oW1+FX/BWz/ggP+yT/AMFadY8FeP8Ax7rHiH4J/Gjwgy6dd/F74a6Potz4j8YeEEs5beLwr4usdVCabrsVjcLp0+jaxqUd3q+h21g+laXcW2nahfQSgH6v/s6ftOfAH9rf4Z6Z8Y/2bfir4U+MHwz1i61GxsPFnhG8luLF7zSr+502/tZ7e7gtNRsbiC8tLiLyr6ztnlRBPCJLeSKV/dq/y8fH3wZ/4LJf8GtHxU8f/ET4MarJ4z/Yq8Z/E3w3pc/jF7XQfGPwy+JOjQ3mj6zY2njLwndJqOq/CXxjq2lpb/DrV/H50Dw3d3WqCbS/COvX1rLpIn/pN/4I4/8AB0X8EP2/fEvi74R/tcab8M/2Rfi/p63OveCNRvvGJ0z4ReNPCttaRNd2K+MvGt/bwaN4x0q5iupriw1q70211ezvdLh8PHUb+LUoLcA/rBoqOKWKeKOeCSOaGaNJYZonWSKWKRQ8ckciEpJHIhDI6kqykMpIINSUAFFFFABRRWfq2raVoOlanruu6np+i6Joun3mraxrGrXltp2laTpWnW0l5qGp6nqF5JDaWGn2FpDNdXl5dTRW1rbRSTzyJEjMADQrxn45/tEfA79mfwHrnxN+PnxS8HfCrwN4c0q91vVte8XavBYRxaXpzW6X11a2K+bqmqC0a7thPFpdlezx+fFuiAdc/wA2v/BV/wD4Onv2Tf2KLS/+HP7JF/4M/a3/AGjNJ8VWejeItGsbzXf+FQ+FNHOnaTrdxqt18R9Ejj0PxcdR03VPsulf8IFrXiGKz1i1ubTXPsRgkVf5Jf2eP2Cv+Cu3/Bxz8d/E3xY+NHjrx14a+Flu178RNM+J3xxj8baT8EfD9v46vYIY/DnwA8JfY5NMkttdsvDcqyS/DjQZPDVnLoNk3ie7spr7TTdAH+ht+wb/AMFmf2Ef+CkHxb+NXwV/Ze+IWt+I/GvwTlu7q/TXdAj0fTPH3hCx1aHQ7j4jfDe7g1LUZda8ELqd7pVsL7WrXw5qpbWdNP8AYwEk5t/zQ/4Owf2F/iF+1/8A8E4ofiF8L49a1vxX+yR4wuPjVP4F0XR7O+l8UeDp9MXQPiBql5qd5rOmLoen+APBc2teOr2eG11ae+ttElsIrWOSZZl+/P8Agkv/AMEVv2Wf+CR3gzxbYfCOXU/iT8VfHtzLH4w+OXjvStKg8d6j4cS7F1pvg/Thp4ktPD3hu0eO0lvtN0U2lrrt9p+naprEF1f2NtPF+uGv6BoXivQdb8LeKNG0vxF4a8S6RqWgeIvD+uWFrqui67oWsWc2nato2r6ZfRT2Wo6Xqdhc3FlqFhdwzWt5aTzW9xFJFI6EA/gm/wCDRX/gp5+xj8B/2cvHf7FPxs+LNh8K/jb43/aTufG3gCHxpCmmeFPHUHxB8L+BvB+j6F4e8RR3Fwi+ILLVfB99JrMGtWmj6XZWN/pNxHq1y1xcRWn9pnxg/bY/ZQ+BXwx8bfF/4j/H34WaV4H+H2g3fiLxHfWXjbw5rF7FYWm1RHZaXpmpXN9fXlzPJDa2tvBCxknmjDtFFvlT+L7/AIKd/wDBnWus3vxB+NX/AATk+IFra6hqV94u8Yj9mbx/PFp+mIWsLK60TwR8J/Fswa2gudV1sawS/j3WtH0LR7e6sLO0u7awttkPwb+xr/wZs/ttfFgeFPFP7XHxH8B/sz+GJPFWo6d42+Hmn6na+PPi7B4Ys4FNrr3hnUPCyeJfhpPNqtxKotbPU/EcU8ENvdG9ggdrcSAGP8AdUh/4OCv+Djbw/wDtO6B8CviXpP7L/g/xT4Z8S+LdV0vVIbfUfC/g34EeEJrH4ReMfGOrG0aw0LUvGHjfQvAUmt+D9Nk16SyOs3ekW+qahYxSa0v+nfX5T/8ABJf/AIJJfAn/AIJF/BPxh8JPg34q8ZfEHVfiN4uTxh8QPiB42kittU8SX+nxXdj4ehj0HTp30HRYND0a6/stf7It7Y6iIlvNQE12zSH9WKAP8wz/AILh/wDBMP8A4KqfBf8A4KmftO/8FE/2evhJ8Qb34f2/jCw/aN8CfH34M3kOo3Xw6t7DSrPS1a+jlFjrdl4z0G40KW+u9P0bS9bhistR0yW2vrqaee3tfDfgb/wWc/4ONviX4Ct/2M/hjc/GT4h+PfihqmpWXhr4ka18L/Ea/tAQXtzKmuvZ+HvinrVzo2k6LFY2mkXEFj9ttFig0+a9tBJJJcoR/qy0UAf5uH/BL7/gir/wU1/az/4KheGv2kP+CxP7OviXx/8ACvSPDGt3Xxg1f9qvUtM1u5+J0dp8PdR8BfDzw/pNrotzq41zVfDOqS+GdRitdRn0a2stG0OW7ivLi5tYrK4/0ePD2gaL4U0DQ/C3hzTbXRvD3hrR9M0DQdIsY/KstK0XRrKDTtL02ziyfKtbGxtoLW3jydkUSLk4rXooA/nG/wCC9v8AwQd+Hn/BUv4cXXxh+FUem+B/22/hx4bNn4E8Y3Je30b4maDpgmurL4b+PLi3jlu7a2t7qSabwt4htoL+Tw9cXmqw/wBkXy6w01l/EH4W/wCCp3/BfX/gjZ4osPhJ8W9d+KHhyx8J/Dzw94C8L/CX9prw/c+Pvhz4S8OXFtp+peF28LpZ6vBpEOuQaNo/2Wwkj168vLfSJdSt57UNI7x/62teTfGD4CfA79oXw/p/hP49fB74YfGnwtpOrx6/pfhz4q+BfDPj/Q9O12G0u7CLWbHSvFOmapY2uqRWN/fWcd/BAl0lreXUCyiKeVXAP8kX9pn9sH/gsN/wcAeL/g78Ode+GniD4vN4bt/E2p/DbwB8Gfhxe+DvAOparpdlq954k8WST6rqs/h678SWWjQajpk+oNrlu/2CyXTI7eS5jCP/AKjv/BL/APZNk/Ye/YI/Zj/ZhvNTk1vWfhl8N7K28Ravc6Da+G9Qu/EGv31/4o1a31LTLPUdXiS90W61t9Aef+07xrqPSo598ayLBF9d/DX4XfDT4M+DdK+Hfwh+H/gv4XeANCa9bRfBPw+8MaN4O8J6Q2pX1xqeotpvh7w/ZafpVi19qV3dX94ba0iNzeXM9zNvmlkdu7oA/is/4OdPiN/wXG+B3xC0T44fsbfEHx98PP2G/h18KrvW/Gfiv4Ha7Homt+FvEK6ho1n4n1L4z/bDAbrTZri60iL4fHSF1KWGI+JheR23mQeb+tf/AAbX/tE/HH9qf/gk98HvjH+0T8T/ABZ8YPijrfxD+Nmmat458baidV8QahYaD8Std0rRrS5vGRC8Om6dbwWdqu0eXBEic4zXrv8AwcDf8oZv+Cgv/ZDJv/Ut8L18d/8ABpl/yhU+BX/ZUf2g/wD1bPiOgD+k+iiigAooooAKKKKAPy3/AOCjP/BYX9iz/gljqPwo0v8Aa18QeP8AQ7v4z2Xi+/8AA6+CfAOp+NUubfwPP4et9eOovp00I05opfE+ki2WXcbkSTFMCFs/F3wD/wCDoX/gkb+0T8Yvh/8ABHwd8W/iD4b8WfErX4fDHhvVPiP8M9V8D+Dhrl5DM2l6fqfifVLz7Dp0+s3scOj6QkwLahrV/p2mwA3F5EDQ/wCDnL9jn4AftC/8EvPj78bviX4Kg1L4r/sr+BdS+IHwY8c2ciWev+GtQv8AV9C0zWtEa98maS48LeIY5LO51zRQYUv73RdFuHmU2CK/80H/AAamf8Ewv2C/2mbDUP2o/wBo/wAS+BPiH8dvhb8Z9Jn+DfwYPxAe3utFbR9D1qdb34g/DPV9Ds7fxHfjUrS38a+DLrw5rGspp8em2Wp6i2n3dobOgD+qX9r7/g48/wCCZP7Dv7RPxF/Zd+PXi/4u6Z8WPhbc6HaeLLLw38I9c8SaLBN4i8M6N4t00WWtWlzHb3qvo2vafJK0aARTtLA2WiYn7N/4J6/8FUv2MP8Agp94U8aeKf2TPiRc+Jp/h5q1tpnjTwd4n0iTwp498OwajEZNF13U/Cd5PNqNt4c8QSQalaaFrMoW11O90XWra3LSadPj+cf/AIO0f+CY/wCyKf2S/i//AMFKtN8DX/h/9qfSdd+CvgzUfEvh/Vhpvh/xjaa5470Lwfcaz410BLRxr/iS18LXEGg6fq8t7C9rpml6VbCJ1s13fRH/AAZ+/stxfB3/AIJqa18b/FPwnsfBvxI/aG+L3iTWNM+IM1pZDxD8RvgfoGieF7b4eSyX9rcTzTeG9K8VXPxGXRrO8W3uLW+utZkNuqXKPIAf1i0UUUAfnL/wUU/4Kn/sl/8ABLfwr8NfGf7WOs+OdF0L4r6/rvhrwjP4J8D6l42ml1Tw7p9jqepJqMOnSxHT4Ra6hbG3mmJWeQvGg3Ia+Qf2Pv8Ag45/4Ja/tu/HXwv+zp8Hfiv4y0r4l+No7lPCFp8TfAOofD/R/EmrQPbrD4a0fV9Xuzb3vibURO0ml6LCrXV9Fa3jwgi2ev0P/bt/YS+AX/BRf9n/AFf9mr9pHT/Et/8ADXWfEHhvxNc/8Ifrw8MeI4dT8LanFqumNZa4LK/ltYHuoUW9hjh/0qAeU7Ac1/iSXuna34J+K/iibwDZ6tPL8MPGOt6tYXFrb3eoTaRpfhDxG8VtqmqzWaB4bG1MNml9fTNb24aZVkljMyggH+yX/wAFFv8Agsz+xD/wS18TfDHwl+1n4i+IWiax8XNC8Q+I/Bsfgr4e6p41guNM8Mahp2maq9/Pp88S2EyXWqWiwwyhmmRndSAhr4e+FX/B1J/wSR+MvxQ+G/wg8EeOPjbceNPir498H/Dbwjb6h8E/EWn2E/ifxz4h07wxoEN9fy3bRWNnLquqWiXN5IpjtoWeZwVQivEPGXwY/Yn/AODgP/gkP8Iv2r/2i7nw/wDEr9oP4L/s1+IfGninxj8H9S/4QjXvAXxt0n4XaprHjz4dalaNaX17p/hBvG1n9rvfDTL/AGfqVzotjcWWpXUNkszfy7/8GiH7HH7P/wC1N+3j8T/Gnxz8FQeO779mT4a+G/i58KtK1SRZfD+n/EFvG1lo+l+I9U0t4ZE1K98NySw654bk82BtK8Q2Gn6pGzvbLGwB/pkftHfBPSv2kfgN8XfgJrnirxj4H0j4veAfEngDUfGHw91dNB8ceGrTxJp02nTax4V1qW1vY9M1yxWYz2F49pcLBOquYnxiv5sf+CXn/Bqf+yt+xB8R9F+Pf7Qnji9/au+M/hfVfFEvhjTda0TT9M+C+m2upT6e3h3XrnwNqNrqes6x410W1h1O0ubrWPE1/wCGL5dWnmPhlLiK2mg/q3ooA5rwl4M8H+AdEg8NeBPCfhrwV4ctZbie20DwloWl+HNEt57uVp7uaDStHtbOxhlup3ea4kjgV5pWaSRmdix6WiigAooooAKKKKACiiigDnPFng7wj480S68M+OfCvhzxn4bvZLaW88P+LND0zxFol3LZ3Ed3aSXWk6xa3lhPJa3UMVzbPLbu0FxFHNEVkRWH8of/AAWX/wCDXH4D/tmp8Sv2j/2Oo4/gn+1bf6JpdxYfDzT5tE0H4FfEXW9E+2m5fU9J/syO48IeJ/Edi2l6HFrek6zYeFtKi0u1vrjwxdXUl9Nd/wBb9FAH+YR8L/24v+Czv/Btr+0D8L/hV+2/beLPjF+zZ4p0jxJ4kHwu1Lxhb+NPCfiyDUpdEXxBqvw6+M1xpt1qVr438Iz2+iWd1p1/qOt+HdBstfeG48ONcajZ3Fv/AF3fsTf8HMH/AAS5/bN1fwt4FT4sy/Af4ra/4c8M3tz4O+NMMfhPRW8a+ItQ0XRB8NfCHjbUnsdN8d+KItd1lLGwh0mxt5NYtbW41K2tY4VMY/X79q39ln4RftmfAX4lfs5fG7Sr/Ufh38VPDcvhXxLJoV5FpHiW30qa+sdQl/sPXmtLybSLp7jT7bdcQRMWRWQqcgj+DP8A4Lo/8G2vwf8A+Cev7OFl+3p/wT+8eeMfBVr+zjrfhfxD8T/DXxH8Y3viDxF/p3jLRdJ8H+Ofh14lt9LjaHxJ4f8AGGp+G4T4eu7Kxs3097/XV15bzTrfSb4A/wBFemSSRxI0krpHGgLPJIyoiKOrMzEKoHckgV/lTfsb/wDB29/wUt/ZxXwX4b+NZ8I/tbfDrwrp/iK11Ky+IE114d+Kni281eTUrzS7/XfjBDaeJb+U6BqF/D9mgPhqQXGkafaaO0sEa/aR4r/wUG/4Obv+CiX7fHgr4pfBFLnwh8DPgD8T9RgibwD8O7O5n8bW3hYW/wBnuPBGrfE5RpF/4n0XVppJZNUjk8Paab5TbQGJRbK8gB/dF/wUG/4OZ/8AgnT+wT41+LHwVudX8WfG/wDaE+FcMNrefDj4eWGPDN14r+3Ja6n4L1X4miLVdF8NeIdCjW7fWrO+025lsLm3SxmiWW5R1/k7+Iur/wDBwF/wce+IvGnj/wCC/hvxf8K/2NbvU9f1/wCEvhS58STfCP4LPo2k3N18M7zw9ZfEmW1jvfiV4ynsnvF8faPca1caDeaude1HTvDuj6fFbafZ/ob/AMEXf+DWH9nP41/sqfDv9qb/AIKCXfj3xd8Q/jPH4W+KPgf4deGPEeq+ErDwd4JuYjqllpXxFTV9JbUfEPiHxKk9r/wk2iXWn2MGhfY59Pt9S1hL97m3/ux8DeBvB/wz8HeGPh98P/DekeD/AAR4L0LSvDHhTwvoFlDp+jaDoGh2MGm6TpWm2cCrHBaWFjbQWsEYBKxRKCWOSQD+Yz/glt/way/sUfsb6P8AD74qftKaNL+0z+0vY6d4V8Q6unjC5hf4S/D74g6Frd1r1vd/D/wrpUFhNqKWsUunaNq8fjfV/GWi63LpUt7BptpaXv2RP6htA8P6B4U0bTvDvhfQ9I8N+H9It1s9J0LQNNstH0bS7RCzJa6dpenQW1lZW6szFYLaCKJSzEKCTWvRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfyjf8F9P+Cf3/AAW1/bL8Zap4K/Yc/aA8MX/7HPxh+F1l4K+Lf7OvifVvAngey03VtCvrW7udQl8R+INC1LxBrsPjWaWK5ePSNR0saI3h1I3aYakAng//AAQZ/wCCf/8AwXx/4JvfFn4d/Bb443HwXv8A/gn2yeOIvFvgSP4ieFPE+qfD7UPEK6p4tTxf8PotBs9J1+58R6t44i07TdSXXNQ13SYdB1rWmt9Kt7lLS7s/7MaKACiiigAooooAKKKKAPx3/wCDgP8A5Q0f8FBv+yE3X/qU+Gq/zVP+CVP/AASE/bn/AG8vhP8AG/8Aar/YR+KFl4Q+Lv7MXi/wx4X8O+E9E8cax8L/AIp+JtW8b6XKt7c+B/iPBqPh/QvC62Phq61dtUl1bxNpD39gl5plubiS9S2n/up/4Oq/+Ch/wr/ZX/4J/wDi39lHxD4d8ReJPip+3B4Q8T+DPAUenbbHQvDnh/wlqnhi+8WeLta1WS2u45pdLu9R8O2Fl4dSO1uNWXVrm7i1G3TSpYrn8xP+DUn/AIK8/sQ/CX9lu2/YQ+NXiXwn8AvjLonjzW9c8N+NfGepaTpGjfHaf4j+OdmgeHdN1RrOyuJPGWi3fiKx0HSfD15d6tdahp8V3fWMtlbW7WhAP54P+Cg3/BOf/gvx+zt+zF4t+Kf7fHi/48av+zVo+ueD7DxVY+O/2zLP4yeG5Nb1rxFY6Z4Re68Bw/FrxZJqM0fiG4sXtLsaJcDTLgJemW3EPnJ/oPf8G4PgDRPAn/BGz9jG60a98T3knj/wPqPj/W18SeLfEniqGy1vVNe1HR7my8MQeIdT1GHwl4YjtdBspbPwl4cTTfDdjfzanqNppkN7q2oz3P4+f8HaP/BTT9kVf2Rvi9/wTZ07x7d+IP2qNX134K+M7/wr4f0s6pong+z0Lx5oXi+50fxtr0NyI/DviS58L28Gvafo9zavNd6XqelXSOI76Mjd/wCDQj/gpB8P/in+yRP+wV8Q/izd6n+0N8GfFPivX/hp4E1+zstPQfs+XGm+H5NL0rwReW6W76/F4U8SQeLdR8Rwywz3ujR+ItLlu72WHUbSKEA/ssor+PX/AIK7f8HFvx0+D/7Vus/8E2P+CaPwM0T47ftSpq9h4YufGtrf3fxFiTxDc+G/FDeMvhv4e+Gnhr+wNXsvij4I1Gzsr576/wDEuqafZrp17Fe+HbjzkaD8vf2Tf+C1n/Bcz/gnF8TfhHZ/8Fcfg18SdW/Zk+N3xm0LwNrHxS/aR8Ct8L/E/hA6vpkunufB3jXTbTRvClponhdSfHviiw1nwlrd9faPo2q21pqelefHdWoB/omV/lW/8G0Hw68O/F3/AILB/tHfDHxboHhvxR4e8dfs2ftceGtT0Hxfo1j4h8M6hHqfi/wlbwx6zoupWt7Y6hZRXLQXDwXFrOu6FXVDIiEf6QH7aH7cvwe/Yv8A2NfiR+2h4t1jQvEHgLwh8PT418F2Y8T2Xh61+Kmq6to7an4C8HeGPEVzaajapqvxDuZLHTPDcwsb0T3GoW7R20+4Rt/lm/8ABBT/AIKM/s7/ALAf/BTHxZ+1n+0tN4u0L4Y+JPhd8ZPDsaeD/Ds3jPXLXXvH3iTwzrOkWb6fazWLy20UGm3sdzfAoiPHGfKHmgKAfcf/AAS4/aE+If8AwRn/AGx/+ChH/BLj9pXVItO+H3xh+G/x7+HEnifUNK0nwF4Qu/ij4G+H/jMfDr4wxeJPiLaaD4kPw78X+GNP8SaH4SttOuvsnibUvF/h24hs72ZbeaL6S/4Mi/8Ak8D9tH/s2zwn/wCrQ0uvqH/g6Q/ZF+Cv7Yn7Fn7PP/BZb9lr4c/EnX/EnxA034aXnxA8U2Ol3UNi37Ofi3wPqGoeDvG3xM8Nyw39x4e1Lw9qy+DfCtheW2oWVja/8JHNYajaahe3NreW3y9/wZF/8ngfto/9m2eE/wD1aGl0Af6R9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfi9/wAHDXgDXfid/wAEb/23vB/hu58OWmr3fgXwZq8U/izxZ4b8E6ILTwt8WPAPirU0m8Q+LdU0fRIL2bTNFvIdI0+a/S91zV3sdD0iC91fUbGyuP2hr8KP+Dl3/lCL+3R/2LHwp/8AV9/CugD+Uf8A4Nh/+CMf/BPn/gpV+yd+0D8U/wBr34SeIPiF418BftDt8P8AwxqWkfFL4leBILHwsPht4I8Rixl07wV4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caption='*C(NC(C([H])=O)*)=O0:R[3]6:R[2]', markush=True, smi='*C(=O)NC(*)C=O', esmi='*C(=O)NC(*)C=O0:R[3]5:R[2]', sru=False, drawing=\"\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\"), TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=996030521, bbox=BBox(x1=0.6827693913359149, y1=0.293701171875, x2=0.711794001089061, y2=0.309326171875), conf=0.84814453125, page_size=[1033, 1462], type='moleculeid', hidden=False, order=10, lang='ko', direction=, source='', bboxes=[], contents=[], text='(III)')])]),\n", + " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1777739452, bbox=BBox(x1=0.17898509347773475, y1=0.37451171875, x2=0.6413056631171346, y2=0.39404296875), conf=0.87158203125, page_size=[1033, 1462], type='paragraph', hidden=False, order=11, lang='ko', direction=, source='', bboxes=[], contents=[], text='并用一种还原剂还原成通式 IV 的化合物'),\n", + " GroupedResult(token='2ff6db102b6d527bb4deef6e5cec442a', 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caption='*C(NC(CNC(C(O*)=O)*)*)=O0:R[3]9:SG[1]11:R[1]12:R[2]', markush=True, smi='*OC(=O)C(*)NCC(*)NC(*)=O', esmi='*OC(=O)C(*)NCC(*)NC(*)=O0:SG[1]5:R[1]9:R[2]12:R[3]', sru=False, drawing=\"\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\"), TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=294740421, bbox=BBox(x1=0.696936165143998, y1=0.452392578125, x2=0.7276884302395934, y2=0.467529296875), conf=0.84814453125, page_size=[1033, 1462], type='moleculeid', hidden=False, order=15, lang='ko', direction=, source='', bboxes=[], contents=[], text='(IV)')])]),\n", + " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1602045523, bbox=BBox(x1=0.12162693610842207, y1=0.533203125, x2=0.9115109586761858, y2=0.6064453125), conf=0.9501953125, page_size=[1033, 1462], type='paragraph', hidden=False, order=16, lang='ko', direction=, source='', bboxes=[], contents=[], text='并与一种酰化剂 $ R^{4}COC1(V) $ 反应并去除保护基 SG1，从而得到如权利要求 1 中所述的通式 I 的化合物，其中取代基 $ R^{1} $ 、 $ R^{2} $ 、 $ R^{3} $ 和 $ R^{4} $ 具有上述含义且 $ SG_{1} $ 是一种保护基。'),\n", + " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1728580647, bbox=BBox(x1=0.12266352931389157, y1=0.6142578125, x2=0.9122020208131656, y2=0.66015625), conf=0.94189453125, page_size=[1033, 1462], type='paragraph', hidden=False, order=17, lang='ko', direction=, source='', bboxes=[], contents=[], text='4. 如权利要求 1 所述的通式 I 的化合物、其具有生理活性的盐或其组合物用于制备治疗疾病的药物制剂的用途。'),\n", + " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=188164908, bbox=BBox(x1=0.12128140503993223, y1=0.66796875, x2=0.9108198965392061, y2=0.740234375), conf=0.95849609375, page_size=[1033, 1462], type='paragraph', hidden=False, order=18, lang='ko', direction=, source='', bboxes=[], contents=[], text='5. 如权利要求 1 所述的通式 I 的化合物、其具有生理活性的盐或其组合物用于制备治疗与血管收缩或其它内皮缩血管肽的生物效能相关的疾病的药物制剂的用途。'),\n", + " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1303007679, bbox=BBox(x1=0.12162693610842207, y1=0.74755859375, x2=0.9122020208131656, y2=0.92822265625), conf=0.97705078125, page_size=[1033, 1462], type='paragraph', hidden=False, order=19, lang='ko', direction=, source='', bboxes=[], contents=[], text='6. 如权利要求 1 所述的通式 I 的化合物、其具有生理活性的盐或其组合物用于制备治疗疾病的药物制剂的用途，所述的疾病选自高血压、肺动脉高血压、心肌梗塞、慢性心衰、心绞痛、急性/慢性肾衰竭、肾功能不全、脑血管痉挛、大脑局部缺血、蛛网膜下出血、偏头痛、哮喘、动脉粥样硬化、内毒素休克、内毒素诱发的器官衰竭、血管内凝血、血管成形术后再狭窄、良性前列腺增生、局部缺血和中毒诱发的肾衰竭或高血压、环孢素诱发的肾衰竭、间充质瘤转移和生'),\n", + " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=430519056, bbox=BBox(x1=0.5276259415839787, y1=0.94677734375, x2=0.5379918736386737, y2=0.95654296875), conf=0.73388671875, page_size=[1033, 1462], type='pagenumber', hidden=False, order=20, lang='ko', direction=, source='', bboxes=[], contents=[], text='2')]" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -132,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "9c965c29", "metadata": {}, "outputs": [ @@ -141,6 +146,8 @@ "output_type": "stream", "text": [ "page\n", + "├─ watermark\n", + "├─ pagebar\n", "├─ image\n", "│ └─ moleculegroup\n", "│ ├─ molecule\n", @@ -159,8 +166,6 @@ "├─ paragraph\n", "├─ paragraph\n", "├─ paragraph\n", - "├─ watermark\n", - "├─ pagebar\n", "└─ pagenumber\n", "\n" ] @@ -172,38 +177,38 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "b8d0d3e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "GroupedResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1317312281, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, x2=0.7110178715040704, y2=0.17773435687792272), conf=0.9677734375, page_size=[1190, 1684], type='moleculegroup', hidden=False, order=1, lang='ko', direction=, source='', level=1, method='default', items=[MoleculeResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1379245026, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, x2=0.4470636960839023, y2=0.17773435687792272), conf=1.0, page_size=[1190, 1684], type='molecule', hidden=False, order=2, lang='ko', direction=, 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caption='*C(N)C(=O)O*0:R[1]6:SG[1]', markush=True, smi='*OC(=O)C(*)N', esmi='*OC(=O)C(*)N0:SG[1]5:R[1]', sru=False, drawing=\"\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\"), TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=894922386, bbox=BBox(x1=0.6876068262947725, y1=0.1339111328125, x2=0.7111029389520813, y2=0.1488037109375), conf=0.8388671875, page_size=[1033, 1462], type='moleculeid', hidden=False, order=5, lang='ko', direction=, source='', bboxes=[], contents=[], text='(II)')])" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pages_tree[2][0].items[0]" + "pages_tree[2][2].items[0] # moleculegroup；[0] 是 watermark，无 items" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "3e73fca9", "metadata": {}, "outputs": [], "source": [ - "mol_group = pages_tree[2][0].items[0]" + "mol_group = pages_tree[2][2].items[0] # [0]=watermark 无 items；[2]=image → moleculegroup" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "id": "e85172c7", "metadata": {}, "outputs": [ @@ -224,17 +229,17 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "ed96d932", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "MoleculeResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1379245026, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, x2=0.4470636960839023, y2=0.17773435687792272), conf=1.0, page_size=[1190, 1684], type='molecule', hidden=False, order=2, lang='ko', direction=, 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caption='*C(N)C(=O)O*0:R[1]6:SG[1]', markush=True, smi='*C(N)C(=O)O*', sru=False, drawing=\" \")" + "MoleculeResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=714662316, bbox=BBox(x1=0.29715671890125844, y1=0.1083984375, x2=0.44711720262584703, y2=0.177978515625), conf=1.0, page_size=[1033, 1462], type='molecule', hidden=False, order=4, lang='ko', direction=, 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caption='*C(N)C(=O)O*0:R[1]6:SG[1]', markush=True, smi='*OC(=O)C(*)N', esmi='*OC(=O)C(*)N0:SG[1]5:R[1]', sru=False, drawing=\"\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\")" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -245,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "id": "72195ba1", "metadata": {}, "outputs": [ @@ -255,7 +260,7 @@ "****C(N)C(=O)O*0:R[1]6:SG[1]***\n", "\n", "\n", - "(11)\n" + "(II)\n" ], "text/plain": [ "" @@ -271,14 +276,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "id": "3b6f4bb3", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -294,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "id": "8d8d7558", "metadata": {}, "outputs": [], @@ -345,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "id": "b3820422", "metadata": {}, "outputs": [], @@ -369,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "id": "fc5b925f", "metadata": {}, "outputs": [ @@ -377,7 +382,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "33\n" + "31\n" ] } ], @@ -393,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "be2994f7", "metadata": {}, "outputs": [ @@ -417,10 +422,10 @@ }, { "data": { - "image/jpeg": 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", 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", 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", 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CHTp0wLaCY15MpAg/SPdYKOMIoeAYNWoU9l5W8MkgIAoPB6GSlBEKHEQBPHNZVPE9JBe7N+KWOSM38wpTKG4jMi2R8SzAvIIYxrgfHdTiIYfceOONpUuXtjKDPIosHYRKUkYocBAFiFiuHTOx07AutdJFMRDCoYAoHDqHBAuvk4Gvv/56Dp+QqM2SZhpFLDd7mDk6LTysSSGJTFCADSFOMlgOsnzhwJyJLrPaB/aV3Mv9wAMPYPOHs70Z6VXBRT4uTf/4xz/8DTzYKm/fvl1XIdShRktkaU0KlwSBqbBlDbFThwvOac4ieA1xp+iEjR+iJo7NOmAIpul5RGriKKhgNeCitMpO/lS0BE300vyuu+46XLK5elLl8xfcMcAk5LPOCZIgdOxB3grKszl4DADLEzrvHi3Vn2uQnfDFAEhsmNIRA4Dl12uaMstR3iYGauZrcSakbSTBCLzSBwM3CqkPnyJdsjEAvAYglPmYwhANL5So5SBS6H5Q+TzxxBM5CJsTJJZQNgXO/HTkEOVHXzqNf4W1bst+2I/m3Htixa/xKy3vkqYAlxNw/RVk5xLcpBvLaAP333+/GdA3hX0jm5hRSrmSXng4DvISJIyY23FUCG9RZxC1xHDF1cY8a3E2gqA4a9YsDm+wYVQ/QgihCUNSdRbObg6nRETkhjKsyXgR4YeQDngQ2lUEXMRptn7sw61eZB22CHLQI7E7U2uqflDr+fPANMW9LiXwoqdFKR2zKXzr0Eqo38yZM9F1wfmoGPnlDjNDFs6E0HqidsZyI1WfOYs4UIzjK9rH95jbGqxFmMLCwxbFDnpUn9iDsiL5ANsQOyZVqHO7oqsXnlf79I6nCt9TajGPCWGPNturcIbzWYGVIQeRybV1dMphIJYA7XNG5dqynC25kiU8mTAAm8zEnGaIFK8IQXXUeyicEqYLRkgIxqo6rMiqwqRMoDXQwdevVKlSbAtVdb6zd999N7yUQGvhqCI8HI5x9MSie/fuqE+Z+p4lgr3geAPtQLCyLqUWLVqkNfy47+ivg0vRAFlbtmzRwGA4gRHyo48+GqBeOIsID4dzXDVWKF2SZ2DdWsIJ87aRhBtxrYiMwEVZUeZh2Q+7TozfMzF2NQMI+hXN1XcsescddxxmPfyc6pBchVrgioMCsg77EYudJPcV+ZXI+XfEYdPnlqiFCA8eHGT8cvSuFc2NUtGTQz77T+fN42howxSOOzihsltSeDi79M9o75hP8AveJZEDlXUBHEtweWWWhHU+doV45PDW0iRhlM8pS5D2OU1NFbezH+aAJ0inoS3DZomf2EsrOvCXCx87deqkxhsVKMuOfhW1hLaXRveb2vuH1Q1DyTMVn5hBgwaxJY7a0Jj4yjpsTyS+69gScS8RL7CJYRtpl5Dn5CjAFX6vvPJKYm2w8mPyoa0pqlSpwm4/saZCU0t42B5KZgl326jcSZMmtW3bNt+3xDaGCT0jnqTkygsWEAwGt23bFhwK+m3atKn2pOds2ZLhgzcVypLCw37DijYIi3O/EtF4h7kyMdDjYjwvwuC8TvA3r7dWPnEtihcvriLLIR9Zb+VRUUB42G8mEPfI9X4NvzohfWcFZEwYS9ZhH6MxhOQGDRroxtmQx2WVqStGKiE87DfceIpgqupXQt7FQwGC73g5FXJVAnsWTrD69OkTT5NS9hDhYZkEmaMAy6xr1Ci8C4cMGRLztoTMAZpXPckeI6+GK8+BxYXwsssuM5HAiJobepGZhYFNssSVlnU4LnJFtDDrJy6sKUH+4osvxstfN4WmqkiRIvpREglQQNbhBIgWuSooojZv3pwStPkcEI1V/4SBk6dq5HgYu/8mTZoowpmRvpMnZYhbQJMc83rOEKOf46hFTpbmwz969OiaNWsyMMT7JSRSjo+QgCcU8KdA5HgYcqAaxanQny7yViiQLxSInCydLwOTdTjZA6fKriPruIQbAOHhcI9v4tgR/EB7syXeitRMPwWEh9NP4xzuYd26dbgQ4c/ghLFs2bJcnqLy8TFwvZvTWUtyMk+BuHkYEYuh7du3b/oiJGWeCtHskesw8crCRor4Hv4UgIcxHfcvI2+zRYG4eZgA/Fi0ImV17dr1pptuSlXo8GzhH81+CQTJ2DGUKloYIYf86cDZ0tChQ/3LyNtsUSBuvfSMGTNYigH3gwM/gqpwWoNzyZw5c7KFg/QbnAItW7Z8++23cajcuHFj8FpSMpcpYPMw/Ll3714fh7v33nuPYN/ff/+9spjDnxv0MMTDcGL8+PFcwVqoUKFcRjgmbJ9//vn555+viu3bty9Vvu8x+01rASLpMF7c6zVt2jTT9U+Nl2toKxAHfQUVxlWuZdIKszQelAIwLT9TA4n1OR9ple/1l4uPuXHD2QdiNgPvVSsv8kuUKGHiNWHChNSGkso8EbjLy/VCUAJiID25wrN69eqxY8dqOsDD+U4EVzTDkfn7OlypUiUcZZV8hWUsm6X27ds3bNiQO5r0QJoJyo8bN47Y3NOnT+dyVP0KXRffbyYH92JxPYfOz/3Ehg0bli9fDpz79+83oW3Xrh3RPHCvadasmZmfF2lCT3HJGPePsQJrgGHIjh07oqZq1KgRQ6zzVWLevHnIVig7YFr9CglcwmhoauRcQn+KWrVqZQGHieyuXbt0AdfEc889h0aECWHVxYYRB/rZs2e71sqpTHXJet26dS0UzEc+SaDD/M4pyH2AWbFiBQC7BsEYMGCAa0UuIqOKqw8gUXhcq0hmLlDgEA0EG13n+BE3EElMl3FNYM2zZMmS/v37m5NepZG3OcBwrZUjmQCPN5wTctccpJKFCxfmCOQ+YCBTuIYf4W4xRgqFlmvdKVOmuGLNNxr1h2sVycwFCvyPh4GG+P0I0iilCImih5MbGfHvYRQRkn0gpgD34lGXn65LolixYmi8Yn4IfFpO+SuUOkCrfhUrVjShBXHg51o9Ngj8br75ZgsdtH2cx1A35VAl3yDMCWA7duywLvj975AUKtSlSxe2CVYvCMxU2b17NzbkpiJTVWEfARFyauws+OURChzEw4oiTPGXX37ZcuhBWr7vvvs4DfbXbVCXX/ny5bknVvMGdeFk6nIsmXWis/AS3xiQ1E8DCYSsXRyVKRQ0KXiEz00FHhUJOg06zO+so6MAYDsAPJhSWUixjSdENkNmIqVhhnUbN25sVYEgiBvcuuJaRdeVRO5QwIWHFXAcQnDlLL7aeparxJgxYxjdmAhs3bq1cuXKVl0m2b/+9a/scvKzzz5rQcUjHyzw8kEKzRDbCqtivXr1OHHxqZWxV88884wFG49cj8RRsBcMLNfNmzd31qpatSqvvGpJfg5SwJOHFawjRoywIoPz2R44cGAQTNiV1a9f3zlLUHqjKQ3SQmrL8FXq1auXuU0ANtDhXG3ixIkx+2JH0K9fP8vkEE0YbaK4jlk9TQWQhAFARzXQ1OYDumrVKq9OWYFdTw0Yrxz5KnlBLvlOChQgSw+8a4KVE/lzwYIF3JGlChDZ7KqrruJkok2bNq5VdCbznrszeUTpbQZbpy7WXWXKlDHPpXWtlCeYzZgK8pflVDfeokULBA14mEgAwQ9OELa5gYl9MrtE3RTXMpUsWdI8UNWv0pcYPnw4h2HIyRzv6V7IVOfbqCcrVKig83WCO82QuokRa9rVFS5cmJvQKINxC4OiC0siPyjgZGvXHCIPMt35aaxQnJQuXXrt2rVBRGtsBqzqtAMbo80OUt0VpCCZaHEAUt9Ar4AHEvgWBV6QFlzLcAxjkoJmOXSlIxbqtKKjgUE+si6CAp4nn3zSR//EeT4Q8v3VI0iCWnwCsJrVLUsi7ygQQ5bW+KCURs/BD/sNcxIQnorJFJMfmNnUxRgTvjWrI6izCMyaNYulXveVqgQiLkp1szvS6JyBxOt8JXjXWKoVLVrUahxOBh00gulAx4TNkl/olK+hV6fko8az9kRAzlhwnpSZj44JvKRTS4HYsrQ1TbnrFcMPNrRsd/UrFjp2mzxyK4+TbXQxEqNGjZo6dSo4rFy5kr/6Fa4UiKOYf+mchBN79uxRAjzWSKwwqh0Uy8rgYf78+cz4hBs3KyKOqlNxlmX2peYrFjfuCnJq9cwyyaSxAOWDqFvgfhN2xfpRJxgjRoovLLsGncnGoUaNGjxydIQ1ns6XRL5SILFPArxRq1YtJ86tW7fGZyBmm6wMHTp0sKrjShGzYswCnLJw27XV8llnnTV37tyYdRMugJW4kxm4U5MFOeE2k6/IbpmDJYsUPHJnr6y9yZM3d1oIKks7ISYEBJb0iKbWLMGuGHsgZ3krB7UKRrm6LgaAXBRqlYn3EatgPiK6TZVAx+Nl2R9v+z7l2Xh369bN6ppDciwrMHv0qZimV8uWLeOU3oKHwySGjK1EmjqVZrNCgcR5WIHLCSTWEdZcYVuI5jYmPoh5TDX1S36D2qlTJ+vcCKgQm9G6xYQkJQXw2XSyMTAgBaCfT0kXARtZv349nVqDAgPv3LkzYAtSLI8okCwPgyrbLXbI7HJNrQnpEw/8OJpKn+RGy4sXL1YdaQZmvwfrorkFqiCCfQpHi9WYTvmZZmrwEp85gKxWrRproL+hW8LA0CyNswOnI/OritUkdleAhOCTcONSMZcpkAIe1uhxtsHphaUx4vQCUTZNbMyWzzrjgZNvvfXWNHWnMY2ZgKM4aHW6DQEtEXBYsWO2EFcBGkRot0jBtwMAWHuzTo24cJHC8VIglTys+uaWd70kKnGOufXPf/4zXshilicgI0K7KTGyAqPQilkxYwW40c+y61LQYu+ZWhictpboKZo2bYp7Rmo7ktZykAIFTR5ISXrQoEF4wCgzpqVLlyrfQyy6dDAXbC2dTo7Bu4Yx4F7Kc2Mt+m0SWDLjWUUCHkZFHLypdJckvOvIkSNVAFBWZmxU/XtEscc5vFcZVN+WsYprSU590SlySkfAStcCkhk2CqT1u0JYDye9OJxke5ZAv0SE4pzTaUKIfjuB1jJcBebE0ALtAD+sNZ29I/Gyd3WSS+fUqVMHI2dXwRgffZrlW8YnA4MZZ+OSE2IKFAA3PUtSnsBSCvN6Ar4Q3sXsiHWYZRlJ2LmFc4VB1cURBwWvWQD9DSc3SmNk5udjGhwxhDTNsJ1YoGtAbeZFNLa+xYsXtzYyzkYkJ2QUSC8PK2JxboTtFIIlj2hHVbRE1hz0tITyIRPTa9eZx9rFJwA7/sGDB1NMz288IgnIiORMADASqpd8/4sKHTtW5RmCFSciMTmsuiZe/jxslpR0hCjA5z+TP+4NRSa0hEYMD5znwzAwLoHOkcBmAyvfTMKcmb60jgAjTaUCxDGDz5xJAVwdXWXpzEAoveQmBVKvlw6CJ5onVlFzdnL/CwbAui5c+sgjj5gFSMP5BG1LuVJXd5rdhOZhU7XORlcTASMNOePN7hjlZu+p10vrOeeTwECfO35MQZEcBMg1a9aoWkS9xXTEagGhmrNfKzPEj9AHd1+NIN+v0GwcNFKSSJ4C2eFhFmHMM/iqsR8eNmyYQgO1M5K2EyXOVG655RbyndFwnIXDlMPhEMF9FUY9evTQt0+ECUfBJQUUyK54gHkgWit2yK661nLlyvEWTWx2gcxM72jd0WPVrl1bCcxEq9PbDRR+ePBnBgzpJe8okJ11WH97ULTyI+gxc5cwkfoiL8yMUFbjZ69Lhj6Bo4KJI1KJ2mughMfj3+nbaBaWdJQpkGUe1qTHogvDD7Z8KofdMu7E+m3UEvh1Ev1TYV29enXsrqJGAcE3OAVyhYeBmAAgOD8FBz2sJZE+sDxToUiwWnV6RIcVccErMQocdMCTWBNSK7UUILIP0TNVm8QqU/dg4RNGPMrUdiSthYMCwsO5NY5vvvlm7969FUzotLRaCw8kgj9ju5pb4Ao0OUAB4eEcGIQ/QECJRQRsfTCOkfmmTZt4iaYa5fz27duJ7IfW9I/i8l8o8F8KCA/n0DwguCdHSgogomFh0aFCoxAMdPLkyeT7eCbmEBoCSmYpIDycWXp798YCa16AxI2h1rV13lXlTaQpIDycK8Pfs2dPvcwSG5SoRlw0iXSNuyWWp7kCpcCRexTIhO9h7mGdixARiZLLnxVkhChBC40TCKF8MPbQGmnsYXz8h3MRK4Ep/RQQHk4/jYP1YPIwwQCI3aOXZd2A8LAmhSQ0BXLIxkPDFM2ECgmqTDswI3cSgSvOzaCzzgKSE00KyDqcQ+OO2Ny5c2cvgHC61j7GXmUkP4IUEB6O4KALyqGigOilQzWcgkwEKSA8HMFBF5RDRQHh4VANpyATQQoID0dw0AXlUFFAeDhUwynIRJACwsMRHHRBOVQUEB4O1XAKMhGkgPBwBAddUA4VBYSHQzWcgkwEKSA8HMFBF5RDRQHh4VANpyATQQoID0dw0AXlUFFAeDhUwynIRJACwsMRHHRBOVQUEB4O1XAKMhGkgPBwBAddUA4VBYSHQzWcgkwEKSA8HMFBF5RDRQHh4VANpyATQQoID0dw0AXlUFFAeDhUwynIRJACwsMRHHRBOVQUEB4O1XAKMhGkgPBwBAddUA4VBYSHQzWcgkwEKSA8HMFBF5RDRQHh4VANpyATQQoID0dw0AXlUFFAeDhUwynIRJACwsMRHHRBOVQUEB4O1XAKMhGkgPBwBAddUA4VBYSHQzWcgkwEKSA8HMFBF5RDRQHh4VANpyATQQoID0dw0AXlUFFAeDhUwynIRJACwsMRHHRBOVQUEB4O1XAKMhGkgPBwBAddUA4VBYSHQzWcgkwEKVAw8zhv2LBhz549wfs97bTTTj/99ODlky/5ww8/rFmzxqudihUrFilSxHq7YsWKX3/99ZRTTjnzzDOtV+bjF1988d5775Hj2ohZ0iv93XffvfHGG7wtXbr0iSee6FWM/I8++ujTTz8lUahQoSpVqviUlFf5TYHfMvtbvnw5ky8ukvXq1SuTMP7yyy99+/b1gfD+++8fPXq0CRLce8QRR1Dl4osvXrdunfnKSr/44ouq5R49evz444/W2yCPH374oWqhdevWfBF8qtx9992q5KmnnupTTF7lOwUOyTACK1euPO+889TcCvg3wzx87733FihQwB82OHbgwIGadEB42GGHqSq1atWCzfQrK6F5mMLffPON9TbIo+ZhWmjevPn333/vWmvZsmXlypVTIAkPu5IoNJmZ5mEI9+6775588slqegX5m0ke7ty58+GHHx4EqqOOOurBBx9U8wDh1qy1ePFir/mRJA8j5FeoUMEEz+tDgKSgi/HF6dq1qxdIkp/vFMgCD0Oy3bt3f+7x27lz5w033KDnX5MmTfbt25cZKiMka1ZkXT3yyCNHjBhhgQk8GrZWrVr99NNPwGbxlRcPb9y48ZhjjtHVvdjPB1k2w5aMgKbAdSk2ebhkyZJfffWVT7PyKq8pkAWdFpP4+OOP11PZTHz77bcPPPDACy+8oDJRxpQqVcqpQDKrpCqNmu2TTz6BJ2kQTm7btu1TTz1lMQyvgAeoWA9JT548mb19z549TRUdIoYXwOy0v/7661QBrNrZunUrMvPChQtRp3m1zHZ97969xx57rFcByc9vCuTUF2jmzJmamgULFkQrkzHwnnjiCd01i5tPv0AFbKrwQw89RMkuXboceujvp3RDhw71qvv+++/rLkiY6zDb12nTpr300ktedVW+cx1WDSIRWBXNdZgyl1xyiVVAHkNDgezI0k7y/fzzz/369WvZsqWe5SzIzmLpywnOw8AAbAiovXv3fu2113jUemmAv+KKKz7++GNXOL14GNn73HPPpS577PHjx7vWVZlePFy2bNklS5aYFV9//fXq1asrYhYtWnTixInmW0mHiQK5wsM333yz5l4SgwcPTuzoJeGxiYuHgc3c9Jo8DPDmK+BhU6001V48jNJO416jRg0fFLx4mOpI9atXrzbrytmSSY0Qp7OzH9ZTViXggXnz5ulMdqF169bV6iWd75VQw2O+VftY8lWmFnTNMsmkgQ0IvVpAskUfpt/u2rWLg6jChQvD+TrTTNx1112rVq1asGABmcFBBUe2Hs2aNUOJQEU+EOjbaEfZwzhpYvYo6VBRIOvfJ9QtNWvW1Nqjo48+esqUKXB1EMA+++wz9Nj9+/fn+MT8de/eHUZSOWXKlKGMufl0toyO6uGHH9bj6r8fdla31mHdjn/CBAldWtWqVQHVX/ow1+FJkybR75dfflm8eHHdETysSPf000/rI2s5H3YOWZhysixLowo2T2tOOOGEcePGBaQvk7V27dp6+vonlPLJq+W333777LPP1i1knoe9ALPyTR7mS6fevvXWWxpyPoWLFi0i39RpCQ9bZAzZ4+/aVD0JMpzgGGn69OmqUw4/kDnbt28fEAYlTN54440By/sUu+iii7CC8CngfIWEbMr/zgJokhAH+F144YXOt8nnzJkzB1tL2sFqGkWaapDZefXVV6PiBiNMspPvRVrIfQpkcz+Mpuf555/XNOLQuGPHjvoxSAI+GTZsWP369Sn82GOPbdq0yVkLBXKfPn1Sa/TPenj77bfT++WXX+7sEb4955xz2BQoNfvSpUuVn8N//vMfIHGWTyyH02nMQhGkORl+/PHHkZzVyRwmMcOHD8emlR372rVrE2tcauURBbLGw6whSNFqckMvVpUSJUokQDhY9LbbbqMi/gbsMJ0toF5iUXLmJ5PToEEDnDfgnwkTJrRr185qipXQ1Hgh8CuZ/4MPPkghD3MUp32k2Aiw6urT9fXr11erVm3btm0WYPIYSgpkh4exWOIsBItLRVN0tkxBf086V+ojOrL9a9q0qetbMxNrKn3WYua7pmkWA0agcn3LIoxzEq/4DO3YsYMEOa4l05oJA5sGYThC3XnnnZUqVdqyZQuaas6Z0tq7NJ47FMgODyNFY8GrqTB//vwEGJjqSKeXXnqpbscnAU/6vOUV6jR+6rPCCnbttdciGrhWqVy5MvKq+eqCCy7wOjcyi6U1zReHX/CjqbQCI41nkgJZ0GlhAoHcy1KcPJ7IyQF5OGZfLVq0wEzypJNOUiVZYFH8bt682az4zjvvkMlhmMrEdhruNQskk54xY4bp1ZRYU4jxwsaJkS5/a2V6HX711Vc7deqkpWhFOM6TWIq9iFinTh2ltXIWQJU9duxYGpw9e7bzrcphhceeWe1IvcqofGzFgASFM4+wKyfMsET58uV1LewZUVDpR75EjRs31o/JJKZOnQqQmGFff/31ybQzZMgQBGzzrDuZ1qRuXlAg0zyMh421uEEmrHl9iIUpohcPU4uzXIwZ8Un2agH9sKlh8iqm8gcMGNCoUSPth/TKgZ9rFbaj3bp1U6/GjBnTsGFDTL5dS8bMZG1nr06nGKXcc889jz76aMwqPgWw4i5WrBjt+JSRV2GiQKZ5OB20+/OBX0paxk+AdQwPQdRa2JC4tomwihswC7J29+MToyVYEtrmzKqO7D1q1CgOpVQ+1mnYlpDGr1gt/myqOROyasX7yIeAWCIcNSmDLaprg614m5LyeUGBTO+Hmf2YDfELQh3Mkilp+s0HqZVkGULJoWfGfoOuTcWvapZDbMRsWE4zsNkdjkcjR470kttBB6mBU2WqsPHWYjmCxpVXXmm245Xm6xAkBApmm6DAORYoEFHQDN/j1bLk5y8FCrDgZB56lghmbUy1FjOe7W7mwdM94j41d+5c/UiCMyqnZA4NQQdZ+pprrrnjjjvM8s40ds6YZyB+n3HGGfotFRHbecRviRCZOt+Z2L59O/EJyCfqiM8Ww1lRcsJKgezwcFipmTBeiodZ9tkMo6JLuB2pGEEKhGE/nO/DhsU4G2PkZOTwNm3a5Ds6An+GKZDp/XCG0cuL7rDlVnI1sWbzAmABMqcoILJ0TgzH/v37cSGWsHU5MRj5BsT/AQT5VVEN12H7AAAAAElFTkSuQmCC", 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", 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XFtavUVXGeZhE87rkKCPpnxxU9UwRSASBRYsWGecLl94/eunPTNT4/HhR7886ZhQyVt2Msp5gBqFKj0SAc92C9RxPUPQqdn6Nq3uEKbDr9mhOaQuadGubJfwOdlSXdsX1nM8//5yVa+jl6U5sFWEtt65bkvtU0j+5Cev5IhA9AeO3HZf0R4VgJUzYtb5EiDNLhYkTZ62PnrD64rlPq6x5lpkvyvrki9lA41kZYC0BiGncb2x81Oj7I7QkduZ3330X9yQzjIGbAGpffvllaMnQHAZn+g+/zNBLlyVH0v+yYNdLRSAsASP98e7Dhd0WsHN/ZvRWgkWW/tgI7e2XX/qj+0azb7Te+D6yiIllq7Z+yZdguRPvMsGyeTveMiwQi6wKj1AZPlOIwMdCDKPqIfo2Oqt58+b9S3NFhDcm36Xff/+dLyTzh8VKOtYWJN+79GQREIFoCDBhNU6AuIDb8qxtQk+OJ8uHH35op8uUJBP5Y4uRYPdWRBPz0TfeeMPmX37pzwY0LD1FPULwGYJ02ppdmgQfRIQGMpGyocPKWJQwCX01HkoVKlQwfcNAQtxNouiEqvgT+tjLVZ4VYRjGzTDGnwvDwKXvl8vVdr1XBLxJAAcQs0DHKf1RWbMwyDXFDJtporxh9XS6O6agqQgsdA52+S+neJvwieHM5MuCiAJGIlAgroN5olk+agrgj2j0OchHZpT2LjxbeJoVl7yaq6H7W9nyyZ0g7h1xMKgD9cQhktGI7wA0QiZ6aFxvx4WfOTL3cuCpmSVLFpbRslkK97LRWFx3eT9/69atjz32GF1pqkpn8QXgXD/s/SaohiIQPALs9oHjO8t9EThmD6go28gnAlNSxg9uJArkP3eZgQ5l0D9ZF9Yfsbc4QcRsJmZSwsfHOyriPI6wsHfhVmgMqpgabCYJnobEj/dpl7gAzkWYH+wIx/iHT71zEaytD41iOmwDbtBkNlug7baAEiIgAiKQ5ASMphrRhEthlA/HoGgkPm6ELBVy3nUxyhvhJHGCtAIaLTamUTKt4wquhyjobYG4EnybWEMEZfB2Z8wggWafrwd7F98EZt8Zm+OFBEuu8Kxi8bSpDEoh1kajRDPxc2wNQcG4iJ2AwmQi+hmQcdRxNtwWVkIEREAEkoqA2REE0eTaozHC8wmPhhBDB0PwY7vS82J5OxQQ+81kERONEJImH2MClljyUWtYPyF7S9gE64OQhrY2aFEYSxh/iLxmrLuE3EkqF/6wFfiXmZjUWQtGtAPUOKYVVatWxXMfiDQE4wQQzdDFL/oihrcoyfzLiul2ERCBGCeACEI0oZGORhNjWLHDF04o7O4bunjoH6sv654QdsTKdwVQY0/IBEl/XmnCcBrRiYhEOW7qYYIH4Jrqiy5kyTFBQ00r+EWVD0SrF8I3n6hBThOKLxqlSoqACPidANNNl6U3QovQXaPxD+uEclHzY2UcFmQ+K1y6Dq6yTswuxbKF403gQoN8JE4D3kjxFvZaAdz2iSBta0UTMISi8Tc5fMFgGnWqs2xJJURABEQg+QigkDdLt6J5BXKbaat1sXHe4o7vj9zHgIkDKRGATTlix5MgVBmCL6HCbsKECRh+iXCAwcE+0Pl6/6YJOh0WqH9bpJqLgAjEFAG39KfxSHmzVZYTBO6YCRX93E6wNrYHYZtZ9OP333+/84E+SrN7jF1qgVWA9QGMkbSoZs2aGgB81I+qqgiIgJNAGOnPbllokWxUOWwFHNgDrMrbeX+8acL1YETFfxRX03gLe62AGfD4yGJfMPOpZTZQ5DvGhNH3WoVVHxEQARGIkoBb789tbB5CVDIWbZmjRYsWUT4rbDFU57j94PNDxEp26QpbxrOZSPnQHVGwXdsgcZ6tuSomAiIgApEJuOf+BCKuVKkShgIOc6dZtmqijzlXcnEV0zNWYsIkRDBBoBvp0KHD4sWLCX4QuSoevMoHEBZzvF09WDdVSQREQAT+DQH33J94O9bP3flcdhVw+u1gG8B/n2ByrAjjEnYCZ2FXmjWxuP1IRe7ColMREAERuIwELkp/FhEQnYZ6sAUKsemtX+POnTtNfB5UN4RjMxVlrRbaDzRCy5YtY2rMgl6iARNf1FzFLXLYsGEmze1mx1p2KPTg4l5TyYT+wsHySei9Ki8CIiACHiFwUfqPGzeOZb3UiWGAbUZ69erFDrEE2WdlFkvLyEemowQnkzSzfuQ+s/6/twfs0QMfGIJAzJgxwzSJ5zA8mDQxqbGXEi0OPZJZM2zyff2Lt4+NgObrhqjyIiACsUzArfmBBVtTsYoVCy0zdzT7hg56GyJFENqMUwLZYwAgrA2rhzlQ63NKpimJTyQ+QibNqgRCHzPrx3Y6fvx4E6KIfW7NVZ/+4hDl05qr2iIgAiJgCVy0+uLnQ5wyFBr4ZRILlChsLPjiQHCPHTuWyTsx31kFZqy76HxQE3Xt2hU5iEYIsU7CKnb4GmAdLAuLeQfb1bJprXkZxmEiCLGjltUL2UooIQIiIAIicIkJXIzvb9+Kb4+N62kyMfYyl2etsC1D8AaMAewyvGTJEsKfoQdH1hs1kS0TNsFz8BEK9aEMW9gLmS1btmRsY8TC+dX6NTH4sYaZBRDowWymF2qrOoiACIhA9ATcHp8u0c+DQjcqQQtEAB/iG2PLJYIbS6KIeFOyZMl430pJH4n+eJujAiIgAiLgXwJu6R9NS9j8Fkf4vn378qHQpk0bnERx+ow1h052Mkjc4udoCKuMCIiACCQ3gcRIf7Y/JMw9NWOHyS5dusSmECTKfyICHyV3d+r5IiACIhAlgcRI/5kzZ5qn4/dpYkHj/s/Kr9gcBqIErWIiIAIi4CkCiZH+bABg2sBOthyk8RB9+umnBwwY4Km2qTIiIAIiIAJxEQjj7x9XUZs/cuRIPGHYWRhHIA5cPNkVEgdQWyAWEix5s8saYqG9aqMIiEDACCRm7p8tWzacfIjuYNY94cYTayZf/gjq168vvX/A/hnUHBGIKQKJkf4GUKZMmWKKlGksO9WYYKWsfI7B5qvJIiACgSGQGM1PYBqfiIYQ64IlDom4UbeIgAiIgKcISPonrDuwc7DSLWH3qLQIiIAIeI+ApH/C+qRs2bIELk3YPSotAiIgAt4jIOmfsD5hfYN1eE3YnSotAiIgAl4iIOmfsN5Yt24dAY4Sdo9Ki4AIiID3CEj6e69PVCMREAERSH4Ckv7xMyaWUQwuaIifi0qIgAj4mYCkf/y9x2ZkbF4WfzmVEAEREAH/EEj8ai//tDHxNS1cuHCuXLnYx8Y+IkuWLDlz5sTzh8B2NlMJERABEfAdAffeXr5rQLJWmD3Lvvnmm4oVKzrfwo5mbIFZtWpVZ6bSIiACIuAvApL+/uov1VYEREAEkoaA9P5Jw1FPEQEREAF/EZD091d/qbYiIAIikDQEZPWNn+OOHTuOHj1KOTx/ihUrliZNmvjvUQkREAER8DYBSf9I/YN197vvvmvYsOG2bdsoh/Rv1apVjhw5unXrljFjxkh36poIiIAIeJuArL5x9g/BnNu1a0c0/wMHDjgLsX0x21iynZkzU2kREAER8BcBzf3D9xfbliH6p0yZwmVc/tmy2OzktWHDhkGDBrGbMWPAa6+9pu29wuNTrgiIgOcJaO4fvotWr15du3bt/fv3FyhQYNq0aeXKlTPl1q9fX7du3a1bt+bJk2f79u2pU6cOf79yRUAERMDbBOTzE75/Zs2ahehPnz49u7db0U/RkiVLIv2Z+BPqefTo0eFvVq4IiIAIeJ6ApH+kLkKxU7x4cVeJXr16scPXqVOnNm/e7LqkUxEQARHwCwFJ/zA9tWXLlnHjxoW5cCGLeJ/58+cnuW/fvuPHj8dVTPkiIAIi4GUCkv5heufkyZPGz6dBgwYZMmRwlSCnU6dOZO7Zs+fYsWOuqzoVAREQAV8QkPSP1E1E90TF7yrBfL9Hjx5kHj58mHHCdVWnIiACIuALAm7R5otKJ3cl8+bNi2mXt2DXDZ3d4wx65MgRrlavXt2ogJK7Pnq+CIiACCQ5AUn/MEgzZ85sjL1EeF6+fLmrxIoVK06fPu3K1KkIiIAI+IuApH/4/nriiSdy586NY0/Lli3nzJnDfN+UYx1As2bNTpw4ge2XkA/hb1auCIiACHiegFZ7he8ixP3QoUMnTpy4bt06/Ds7duxoDAAE/JkwYQL3MDyMGjVKa33D41OuCIiA5wlI+kfqov79+/fu3fvs2bPOQkj8tm3b9uvXT4HenFiUFgER8BcBSf9I/YXenzAPBHQjvI8t17x5cz4LFOfZAlFCBETAjwQk/ePvNYL7O828mTJlSps2bfy3qYQIiIAIeJiApL+HO0dVEwEREIFkI/D/1Kplv1jJYj8AAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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+pKqo8jEgKeJ30qRJlE6ZMoW0AvD9A15pgXZ+bbLB3LlzFcAScRTI3YenrOXMKMA3sQLf8ObLJ/LIB1qXL19Oy2vXruWbvXzw5NfCKlKLX3Iold6vvfZaAXAXwYwZMzLDLWm1zGvuLpsCp+fPn8+Hqvk2EFu+i4p7Gmg+X5O78cYb+SZjoDTwcTk5PbSWJcpEAWOPMpErh8Ds6L169Vq8eLH0QVBJ+/btSbPcb731VrbtQN8tW7acOnXqhAkTLrnkEpjKLe3Zsycnhpvjptu1a9ewYUM3x9IpKZC047Jox3vppZfqJPEV0nnz5gmqsAffx3JLRbjSgUyePLl69epat0+fPitWrNBSCdzS0rZt2y5cuFBLLRFPAVPNdeUUS6J27dp874bTQxBC3+jevbucJJEo8mGtmTNnalGbNm34JqO+ugk+kw2nNWvWzM20dAwFjD1iiFOYIj4H1aRJk0DffNh6wIABgUx9bdGixYsvviifge/WrduCBQu0SBONGjWaM2dO/fr1NccSaSlg7JGWRPkG2LBhw/vvvx/oFV0CsyyfdecJ2LWAJOK9efPmGHbr1Kmzbt06PgDPL/l8/pdvZJPgCOK72DVq1Ag0a69pKBAve1lp3igwbtw4FrfMFrrHqF+f0aNHY33yREM/7YsQRZWRI0dKg506dYLrPBsxMKWA3fdIs33ks3j8+PG9e/deuXKl2ylnxZVXXqk3oviob7169VwAN434RAso3yjraPOYffESAsB3nxGuXEhLe1FAGcUSxUABlGzx/aWaPJyG8XjiKAzUxT2iXsL4ulYaoIDpHoG1VOBX7E5LlizBHY6YJN5ufl1lA6ehG1QSRhdgPWoohdn4Krxo7WFgy4mngAlX8fQpWCn6Bo90v3Tp0tatWyN0CWPg96hVq1YqzPCT4PibPXs2GyEwf/vb3/r37+8yTKqKlh+mgJ0eYZoUJmfRokXTpk2bPn26LGt2fT09UBs4T/r16yeYzZo1KwZFTg8A1FFIO8YbMeSKL0oZehBfzUqzToERI0YMGTKExb1+/XpXmgp31LVrV3SJcL7lZJ0CdnpknaQ5bxAuynkf1sFGChh7FNdCQN/o2LFjGCcstsOGDZP8zp07hwEsJxcUMPbIBVXL3CbXlXiohuLx73//m6CpK664Al2cB883iniHDh2WLVuWtl0EM6qo1xx4ySEzbV0DCFPALFdhmuQ7Z82aNQMHDhw8eLBPx02bNiWYt2bNmi4wZ85rr71Gzj333DN8+HC3SNIo6ISZVK1aNVxkOTEUMPaIIU6eirja2qpVq7Sd7bfffngwCEzcbbfdAsCrVq0KMEwAwNgjQBDPVxOuPAmVQzAuJxGUHtMBwtWgQYOGDh1KHFaYN6hYrVq1vn37xrSAqBZvDYupm+QiOz2KYvZx/L311luRqBCgPnHiRNyCkaWauXr1ak6hsWPHjhkzRjNJ9OjRo0uXLkcddZQ5zl2yeKaNPTwJVTAwNPK6det6do8KHgg5QcuPl7s8W04mmLFHMufdRu1FAdM9vMhkQMmkgLFHMufdRu1FAWMPLzIZUDIpYOyRzHm3UXtRwNjDi0wGlEwKGHskc95t1F4UMPbwIpMBJZMCxh7JnHcbtRcFjD28yGRAyaSAsUcy591G7UUBYw8vMhlQMilg7JHMebdRe1HA2MOLTAaUTAoYeyRz3m3UXhQw9vAikwElkwLGHsmcdxu1FwWMPbzIZEDJpICxRzLn3UbtRQFjDy8yGVAyKWDskcx5t1F7UcDYw4tMBpRMChh7JHPebdReFDD28CKTASWTAsYeyZx3G7UXBYw9vMhkQMmkgLFHMufdRu1FAWMPLzIZUDIpYOyRzHm3UXtRwNjDi0wGlEwKGHskc95t1F4UMPbwIpMBJZMCxh7JnHcbtRcFjD28yGRAyaSAsUcy591G7UUBYw8vMhlQMilg7JHMebdRe1HA2MOLTAaUTAoYeyRz3m3UXhQw9vAikwElkwLGHsmcdxu1FwWMPbzIZEDJpICxRzLn3UbtRQFjDy8yGVAyKVCFYX/77bdffvmljL9evXqVK5cMzyxbtgy069atW5yT99NPP3388ceKW506dapU+ZngPs+qVatWrFghkNSirk8tg8kuBX6erZkzZ55wwgnS7qRJk0477bT4PjZs2EAVYPbYY4/dd989HjjrpSyaF198UZrt1q0biQcffFBeDz/88O22207S+guqIMxrkyZNGjdurPmRiffee48Wdtppp8hSn8zPP//81VdfFch169a5xLzvvvt69OiRdvdZv379rFmz/vnPf44YMULa2WWXXR544AHS++67L/uXZObtV0fUsmXLrbfeOqbfV1555X//+1/79u3TjjGmkeIqYoebMmWK4rTllluSE/MwZ9ddd53ADxo0KAYy60U//vjjzTff3KtXL8U2kKBozZo1gX5r1aolYNdff32gyH3lIGI4Rx99dPfu3b/++mu3yD/91Vdfde3aNYCV+3rDDTeMGjUqpsGhQ4deffXVbhU3fcYZZ4BnTPVcFD366KOCw+uvvx7ffseOHStVqnTHHXfEg5VQ6WbgunTp0jPPPFNIsPnmmzM9MQNo2rSpQB555JFp6RXTTlmLIDoLN+221KVLlyuuuMJt3JM9XnrpJV2InTp14sBxG/FJs+u7Z4W2FkjsuOOOHCPhBseNG3fuuedutdVWAfjA64knnvjHP/4xXD1HOZ9++mmbNm0Eh1NOOYXzMKYj2API2rVrx8CUVtHP7MHDxqnTAANIZvj38ssvr1atmkAiJ4QBcpTDnlqzZk3FUBNTp07lQFchRPIbNWrkopEBe8CErHW3EZ80+4UiFp/YYYcd7r///kCbl156abjWyJEjGSDPXXfdpaWgd+qppwaq5+j1nXfe0X5JrFy5MqajCsseaOcix0MCNIq1a9eGqTB48GBXs8wbe3z33XeXXXaZO0nnn3/+NxsfxC3wZKe/7bbbqlatKjCyehiRDCED9njuueek5TARUuW0bt0auUIQYAcZPXq0YBj4RXZVmGnTpklr33///bBhwxR/2jn++OOlIkUCwxgHDhy4xRZbSPVUc5QKvYzzE84evxipmFHUVjnZUU85wT/77DOZCf1lQ2W2eEUAO+iggxo0aKBFOU2geSNZSRfwJ12DKocJj6xIMjnWUC1q1KgBGCt78uTJf/3rXzPGqlWrVqLN+7cgOo/AX3PNNWeffbZgGPhFtxYYiLlo0SI4n9cXXnihT58+kmZExxxzzFNPPSUVIbXAM8b+Gx+dI93OBCBHvywMTuMcNV4Czbr7CitM9zCO7w8++EBLSZMj40ED1vw8JBAwlI7xci37a/Xq1QWYNSq4ZXB6ZKB7HHroodJvw4YNYc5UZFm9ejVbjw5HKDxnzhzNgR/0xIhsRDs65JBDFixYEAmT3UxVzUEyocKVEvSWW27RqUJZJB8phaV28skna36k6KUtuIlnn32WukuWLHEzJc1mefvtt4fzwznKHuysaa0iAACG+oRtVJrKgD3cfSGMTzhn4sSJ4pfAIsxiCgO4OWIiQ9njwUSGf4OjRmnLCREv12H4UgkNbQcjstt4LtLGHpuoytLXqdp///07dOiA2VtzJOHDHvgZqStmrubNm5MOPFjHkYXIHDNmzKbuo1LKHj4a8w8//IBM//LLL2tLeWAP1DChTIxVQ/EJJALkxY0YAAi/6ojoVLawMEwWczzZg2kSf1H8CZ9FxPLQ1GaBPti62HdlsuVXfdKsznvvvfejjz6K395oEPFg1113dRuJSce7I1577bXtt99eqs+dOzdt14Hh8KqLadttt62f+tl5550VSff0gN/QQxBppOqAAQN4DaBRTvZQBWPChAnxkpWMTkcEwnlmD/if4bsPfkAMVuPHj0dZEgJWJPaoomtCEkgm+GjdTAncIOfaa6/FZASAW1rONHKCkjXcFKsQn8zy5culCAGmPL2LLSjcS3zOVVdddeedd4pNAsgbb7wRDeeRRx5B2mS/iK/rU8piwq+P4Qtgdl9lFZ+6+YfBnKCinfbODoKFncnSnAqTCLIHA0PmOfDAAzUyQobK0txzzz09VydzjNbomlag3dNPP83m5xKOjvr16xdp7xcwFrSryLp185OGOT/88EPlDTplIDy4/xCKWNlZQUNZYv78+QRuqGcpVeO49gk5SVWa63yXGtpXYGY1v9QTEeyBXIQttXfv3mpRYVdD5T3rrLM8RwuxcHvJjihV2GBgD7c6lkoiLAIODRcgK2lClTAthJtiRBdccIHm33rrrTrrnTt31sCtJ598kiA0BXMT+OkIf7rwwgvdzHKm2SyISUkbVTV27Fh1rj/++ONYPvz7Pfjgg9H3/OETDhnBHlBkn332adeunbIH4pY/b1Cd/RVJDJaIIS4s9MYbb+AOw+QfA1bWoksuuQRbEEYFOZTuvvtu4iCkEQzThEVIGvFd0+RgjWU7kCJkOWEV/D949yQTOYpIEHxkatlDxMIhUH72wFSKCUt68fx1z1vUFR7PioChPCArZswh//jHP9R0HugUDZ7dJJBZ8q8s08gHHVTHtt9++0XCpMpkeanAoI1EJk4//fRUjZCvWofUdTXmyFqoyBxKAKPPEIcCjHoJyIyxAYQ7IrgQYVJxxhoGz8+YMUNzSEj0CuckQSKS72O5WrhwIZ5NHqzeYOharoiOwd4dOTQ301XNXXw805xO8+bNcxuMT3tarrDgCwIVWTUPk5gY5ueffz6cH5/DAtXTA4+ypt1awIT1PBcAIQczGqK2ZOJTc0sDaYSot99+WzZ+TM+s7wBAmV5p591336UKmyWuUrzdHEqoZEQrIxOST6aoZwTYcwJ4No4yg5kbggAfroWRUNg7VWuchFiNWM0KAAElPGybbbbRzMgE3bFnoe8xED1RIyFTZVLXU/lM1ULJ5UcLV+4woAh0cXPSppkGdz8+77zzAoo+LaCApuU6ukZJIHoFazJVWJ3Mq/r1A2igxhCdIZnHHnuse/oFIH1eWaacHnAIkg/CDOwNwgEml8sP9Pvmm2/iraNZViqqfEwUBseLLs3FixdLIInic9JJJ3FMEVQSuQo5Z3r27ImyIfDsHXQEejgcsC6qJKytuQmOviOOOAKZWZ1IbqlnmvmKv+/h2U4JgaVnj/IPpjxT0qxZM4QiGAw02DWxqMZfqCg/ttICzhYkaRYfCJCDX9zds8kh8CS8iNGSyUdLgZOlHfcXc5PLYCx0NDpOJ/wGBFkJJGl4LFI0hYzKGwC3bdtWxJ7jjjvO7SUyje6kd8giAVJlYjx85plnUpVW/PxUcqfuvpzaqWDyk+9yFxab4cOHh/tFjscmI7OFaP7QQw8JTMa6R6CLsDWJvVxh1C0oCETGemAMVP+mgHELTVpwN35YDqVfW9YEVgHXEsihCm9oae4SbsRuzPUeTNKcTjKuZOkeMuYC/iIpccmJFQ8OKBXc1gpb/dm28VgBgFBEqbuSsoI5ttRPPvnEPbjY+JH65I4r8hUefdFV6A5ePeeccwK3wxEvXYETjjrssMMEN2QezHcY8XhloaPbzJ49O4A2QTrc+pBMxDzMzYQVB2A8XzEAKAEPOOAALmCmqsg55pq/U4GRD+ekFZVjqhdvUaqNh4BwER4KfnqAIeovMVphYSZMVo4XBBgdFLfPBYa6LDvNDyRYuK48IyYy5HVWP3E0AoyyDou6nnKxXEkpHcVr1YoqmMBaCC0uDqjL6BVpB0jv9IKq49Yta5rgf+0ICS2mOhTANKKUiTk9sJpcfPHFMkYMDDFtllZRMOZKsWeRXXTRRdy/8TFWaq3cJVidHN86r7raNIFMRVgU11RcHKiF0Yl8dkGXbVwYSbM3i/CD3IIlAGC2WJYjpiq9WcVyYetV9dRlDzpiidMRd2UVpXAClyvaRSQmZOKRpIVwLXJQUSgitEksV2H8/XPoSO/HxrMHbTJkdDC65sEwGNOLGHahWPi6f0ytIi+qBH6R8yGZOMuGDBkSb36NqZ7dIlDFtsNCjGwW+aScESjTp08nOICoKsSeL774QoMUET/69u2rsR7sGnjHwAH24JwJIIOURQuBTH197LHHtB3NdBMsR0bBr5tJGpRimg0Ap33F5yv/NQN7BJw5aeumAsB2x+nK/vXEE094HqSpmiqi/CJn30Khx1HgXsNA4cGKJeGx6il3T49C4ZlZv3CFLMG0p0dm7VeYWvkw7BbRZuCNChIU99fZCyWu5KabbqIq2jYnTIXRQRFHiUf2JkkSASsncdB+Y8ZA2aJFCxeWf7VBWRcTGVqZ64VwwUoljRkj4+CrUhljOfFMo3uUs/VSr45XWwJ+Ef1R+lF75DYlYQTEUyJclegAGQVjQY1WM0OJDiTXaP8f+L9F5YNovU8AAAAASUVORK5CYII=", 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", 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", 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xkFr3c8KRWDtH+XqmzeBWGC0Zj+Gn/F5zzTXscTJtHJNK9G44JrF8NitlhtC0adMsfBH3LNbdVrajEGNXFL0UycJYMFowX3fddZdZCLDrQox5+xkzZgyaK06+9M4pZmHJAC9KKguBuCHAUt1k0Y+hDIBnolTPrgxAP3XPPfew7ubC7PDll19GBOWrr77q0aMHhlzDgKpXr24CgAMMvxH7CW7AkLBxW3UKrJlt0YoVK4JbZr9m2rRpl112mWXcaKWIu8vyENhMPPLII0Z6AQtgoiPKPpGZ9oCD8IUXXsgnUCzyrWuvvZaM1l7Kp06disctxPD0kksuQQxA3rBhw9ixUUmY2Pr16zPtWZVCQAjkEALkyDG2X+dkAGZbDAtEluK4ycoRDQxSIQwKBHZ5PVvRmKPGCdM+C4/gcYTAoZMxy3O0NFg4ly5ditk5C70FvILVF/1VnTp1rPs8YoCwLzZDAS0P9daIAZA0koCEDf369QsP5qF+gvYommDr5hOIAbQ9aAMD+rngggtMA34RA6jUcCFgl2Mr0SAFvKJbISAEchQBvF2i3AeMGjXKsBFYUxi/IKiNvA+A3wUziIBx0guePFxwK1QxfHX48OEBbbiF/8I4yD1w5plnwkpgcChqoNW48QS3z34NLBVTJ8pus1THueW6664jPivLPYNGly5drrjiCsMKkcm4/dAhXkCx4tSAiYMXrN8IGCgHJQQMY8ky2fbFTz75BPyRXtDPJowwEyRlpnLx7bffZmrMhbGdYGz+DFh9/Fs3ipx9tlsVhIAQiAMCEWUAimizm4/SNxSacWDBdSVzXZBhoCS3wUhr//Pffffd8EM1MUReF1TTHnf4gQMHWgdzeBDfjglfC08PT2HcWGhheYZxswSO+EqoBij6EV30gzRG80PuVlbHcO1Q7bNcT5+Eg6NoMjSzm2EfQ6xZljvEUQz3U++5BU8//bTXCpTlnvWiEBAC8UEgogzAeme0EWFkAHpy3NAtwY8//jhMJnMZgArY6joMJ+KXZS+rQpxDvAEFpjvkz3PPPYcuiGbBMgCbpwnmwv7AwjzLzj+W9EMqwFI5xh1HTD7NQv6Q3vU2huOjX6ITwrW92nNvmxiWMduSIZbPGcFDPgycR5cvX35IshPDL/OFOwEqHaYGByQ6ZHtEfQxJVVdCQAjkNAL8499yyy38F+Of7eXA1i/IygDWfKT4pWWwLojsatY+yqIWVTzNMpcByJOhQ4eWLVuWFqxDURxzGaUKNXCigAGjHDACgKfBMoDGqBcGDBjgFUEBPcT/Fj7OMLmCPXCA2zyKA6+POHC2UMawAbawchIQLVy4MOJbNABw7O28xcXehRkkeCKaF9VGCAgBBxEwqcb4R2YZZ8mzMoAVHlzL1OPXx389SSNQhFCDyhdXDmq8zJmFfmpqKpU4Z3q1Av/xDSWhDdwHfxWWjVyoI3Dh5x20Q5YCU0Ahjr4YFXPAZwKaOXVLUDHxulwEo3khwAMStYl5RMwBA08s2YgoArUwuYOtuZgFuDlq+lCEIbpIFG6iw3kF8Uz0HAPxLh9Cvat6ISAE3EQAJQ8pyPiP9uozjAxgE/DOO+9Y8x7niJGZGCZgB0KZRTycxNZ88803ZnFJXLF9kaeBMgD24V28GwrgR7Yjb4EPQ59X1HifulbmOBeTtIfFNQdjffTRR4ZCDtxhJ4WwZSzISbyMXKAcf54ZM2YQQmHTZmDdNUkmAshjw0i8sWmGBonTgki9553jgPa6FQJCwC8IsBCHL2GPtDph1q9oWcizGX4IhIPNmjXLq/MgKzOsD14RsMwNJwNY7CNboIAsnvZ7aNu5uIXLzJ0710cyAJpxlTH6LsjGScYOipFy7A5CEglpK10oADVeQ9jqIZgL3yQS51mvJJ7i6c+KwDwlMRF7GjM7LhAvGoSAEMgmAkYGwLuxcWanKzLzY52FgRCtFdBPYM44OAirezyCZs6cef7557PdgL9Y/0hEEDkG2BzQgMylqKoN9/HLL+py6y1D2BqxC4ZyuD+OVujQvSl0XBgUc8ZWgPA0CMPPCrlLUlJs/cwOU4Ddm3p2DFCOjz95tuvWrcsrLlAuGoSAEMg+Ahh1UVMbrp3l3uD7MAeUSChCgn1/AvcBwZ9BRY6HjCECpolE8rZhEcqRvwGCxeVbeCUcH5MAURXELpiQCggmMpsavFqdJZ5YXy/y3jKHAEcM7HB2XCJMCAiBMAig7GWLjw4jTJvwj0gMw9KQ/DQ4DQa3jLxmZAdhHD1hOggluKeVJNiX0aiwXfDyI/fLwHHvvfdeeumlZNDEI9N9gg2FhPiGIpXUntaJK1Qb1QsBIeBHBNAGk6zF+Plkjf6HH36YzI948+MpHtzDQRmAKRg/U0yjaKBwBiUJhDGTet9h1U9aG4yoNOBCa2Scjbxt3C8jV/G0IRwXO6oJmoBmzushcabLnNQah3HHwkJD9rpM81S7j78oFAJCIHoEYEoEvWZHx8vrZHPAESbTjx48P4BQVewG+AWR2p4gAuyQpDGgJuA1dEHWBzHgkfu3WFBRjbEDAFbEAGMheRGmc9hr3759kQ3mLDCXBwKpiGcznaR1c5lU0SYEhIAjCFhdTjA9/+wD0CaTZM37GKU5rjLemlxQXrt2rT1LE2nXvn177CTEDeBf7/7oEGAQSfBwwN4LgY2+zn36RaEQEAIOIvDPPiA9PZ20ENCH5wlcErZCmV+SR5JYmGhjOCYZQ0nE5uAYskYSmTDILIReC+0WAo9wqqz1E7e3UP7wLXYwVilkPo2px2bPjhsx+pAQEAK5A4F/9gGoRPAKZUhEIuCB/uyzz2I+xthLIjMCCvD8wTsVt/TcMWY7CnRB+LkyUsaLecPWu1moUKGCm4SJKiEgBPyLwD/7AHxO0IegEWIkNWrUQElifEDZH+A8w24AXbk3obx/BwzlGDlY9bOzody9e3duSYXt/ohMRJ77dIpCISAEfITAPzIAAylBBMF0c/gk3p+4hHoTQQc380UNKXRGjhwJqSRg4mBLxB45MFCtcKhZRkYG4W++GIWIFAJCQAjEEIGDfkGhOkUMhHrkr3oOcDfpllD+kPfUSjX2PZwNgFQwWx9/DUrUCgEhIASyg0BkGZCd3p16l9xHKSkpmDc4K5HMEPZ0e8IgSJWKHsx9s7BTeIoYISAEcgECSSQDCLI47cBFUp3gmSNRBFdwvfs1uclZy320RaEQyGUIHIwTzmUDS57h5L4wjuSZO41UCCQcAcmAhE9BdglAu5XdLvS+EBACyYpAEumCMAWTXt+4P5111lnk2iTlMpHDZIkws08ILrk57Xn0fvmTwG2XPE4cfewXgkWnEBAC7iCQRDKASLdWrVpxTBjoE2pLfoUmTZqQfJ9sSmY+OB0Tu7E5mdOdGYpICX695A2N2EwNhIAQEALBCCSRLojkECZxEjkwrrrqKrNwJjVex44dMRdjECahNLmDgjFyvIbdjE+t2Y4DK/KEQDIgkEQyAHbP2WFM6l133TVp0iSTIpTgZ9Jq41pzxhln9OrVi4OFk2HWNUYhIASEgEEgiWQAAzZRYPyagoHAW/bjn0Wm50L4cSCiWQgIgfgjkFwyIP74xuGL2Lrj8BV9QggIgVyJQBLZhO38zZs3z5YpkCrVJMvzVvqoTNizj6gVqUJACDiFQDLKgLcPXE5Ng4gRAkJACCQEgWSUAQUKFPAGAaBL+fnnnxOCvj4qBISAEEgsAskoA9LS0u6++26L+86dOwkZs7cqCAEhIASSB4FklAGcGcBWwM4xZ8r73TXIjkUFISAEhMAhIZBEfkHofMzZ8fx6fWnMLQcpIwwOCTtHGiPSHKFEZAgBIeA7BJJIBnz44YcPPvggM0SCIM4U4xgZytOnT7/++ut37dq1evXqW2+9lXwSzk5hzZo1M6WNA9EyrVelEBACQiAiAkkkAyZMmLBlyxYQgePffvvtS5cuZTfAecIzZsygsG/fPk6anDhxYkTIEtUAWUVOixIlSgQQULBgwYAa3QoBISAEokQgiWQAGeLIDEG2OK7KlSuTFgIzQPPmzU0NvyQReuqpp6IELv7NyA3HDsa76uc0NBycZMyI/1zoi0Ig1yCQx6sZzzWjynQgjHTjxo3o/XkK6+R4YQpEh5nNAWWSyvnrTK79+/dzeAA7A5kEMp1xVQoBIRARgSSSARGxUAMhIASEQLIhkES6oGSbWo1XCAgBIRARAcmAiBA512DNmjXO0SSChIAQ8CcCyRgj5s+ZOgzPpW3btvXu3XvOnDm1atWqWLFily5d8uXLlz9/fp+OSGQLASGQcARkD0j4FERLwKBBg4hv2L17tzHjc3wYlu26deuOHTu2aNGi0faidkJACAgBDwKSAR4wHC6yA2jQoMGqVatwXipXrhyU/vrrr2vXrqWwYsWKatWqOUy7SBMCQsBdBGQPcHduLGU7duxo167dl19+2b59+8mTJy87cKERql69Om1efvll1ES2sQpCQAgIgegRkAyIHquEtSSYefbs2ZwZOXz48IYNGxo6Tj755FatWuXNm7dfv35TpkxJGHH6sBAQAn5GQDLA9dnbs2fPpk2bsAGULl3ae+wBdHfu3LlUqVKkutu7d6/rwxB9QkAIOImAZICT0+IhatGiRRkZGVT06NHDU/2/IqHO9957LwUT/BzwVLdCQAgIgYgISAZEhCjBDdgBcEFEr169gkkxyYLS09M5CSf4qWqEgBAQAuERkAwIj0/inx599NFGBdSiRYtQ1HAWprYCocBRvRAQAmEQkAwIA44Tj+rUqUN+UEg5++yzQxFUv359gsVCPVW9EBACQiAUApIBoZBxpZ4zA7igxkQDeMniBLT169dTY5JIex+pLASEgBCIBgHJgGhQSnCba665Bo0Q595w6pmxDRiCBgwYQPAwAcM8TTCJ+rwQEAL+REAywAfzVq9evdTU1O3btxMphhgwFHMWJvFiHCGQkpLSunVrHwxDJAoBIeAeAsoV4d6cZEYRB9106NBh1qxZxYoVK1+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", 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dGASwtbPlL+IA2MqbkkuslSiL2ZKD6KdPn47J0xIjIjeRZsaMGZbHqcVNq1atUidhATWPq/+fTPoJZ7yY/QQFgWX58uVOWCyUnJcs4lj+dLwRfdsNtPyAgEUVEMw+rLoxawI6WZwBnHKtTWcY0du3b58Wq9u4TUZsL5ObOCAlIhQoePPNNysMY42bcJi0gFaXkDoPni7aGr6SZs2aNfpu/AGGTtjnzzvvPLNSxvbffPNN/IUkISXyrF+/3vo2BiRiVr1jxw4sBWpRBnWeeOIJsmh/MwtYi5uYz5oIlChRgtLMwn0Shnc2bNiAXn379jUFhlLxmciKkNu3b6cnsESgi2V9nYr4+XB2w8sDwfBa0tISgKRQgV8CxKSgY/REdhY6zWLvuOMO6or45GcFcLfyqkeDiTw+384yJ02apLmbMyZRhF1cZjIalwms0pdnh/NLzLtn+F5yckDZsmVNaFTY4iaeNJPjceTJyrOEERG+NCtlXYNVLZ/4aq5btw4FTfEI4/tuoWwdM6DSY+xXWAMsK566EIubGJZaS1f0URDAjm42VcrD77zzjjmgRh38ABhoP//8867I9u677zpX+jBpRXvlulJpZgvBF0RvgVANypMJCHxPKLNFRUyP8cXkaFUFh+77bQDFlkC6qH40InITClp7fbECkUv/zLtM7S1AzuAm7kU8BY3+Yc3a8HW+99572TlHO2V9ZYHVU3TTj64K4OMf0YvKUsC7S6yYjz/+OLZJSzBoiCVLq96I3MRURc9x6L66HIubKIpzkJ2j0WbNmqkN0lZdKbmEZ51HMIOPu8K89957DBs1UCrAepYegbpbXWZLY0Mcz4IlHtalzJYTOz305IQaF7lRo0bFzpi0uzwazDdNHKJxE1spOYTSTBktzGYmS36bm7B9sBnF+kW0gDDJdNF2ywogszlLbpZUeU1ZEifnEtek66+/3pKHSyIjDmcichPDVHYUKoFjcxNpsDg4D4GCsNQhUMnROlot7Dmw5hpAMXz4cC9e5nxKwzI/URff7IkmW9LicZd1UgZNzOPnugx4VFhDVECoVq2a6xUlUCCPBg8m8ugf5o6PP/44WlGYLJydR+dVARYrTauRKsrmpmgVJCEemyhzw/nz52urDXIzyGc2xMtKD0C8loQRIv6B5uQCeZjVYmpBvGhLwljx+QQWydSPtbnLLruM6SrHPiiBGQOSHdMMCSxPPK0R80TSYFE2EeBYKBC47bbbGDskDQQtEovc1I7YujMhP77+yEkf1cncDTCdp3y8TPTzSaWIwY9NHkkeQ4E5W3CoWhsckQp52BCHkNH8JLIICJVSOD++e6RBYMHhNAbnM3TIYvkJZEckkHc+GpybmKH5heG/+lCT2bHpUcDItA41I/YlH3GTwgsIOKyWB1J3BXRAJU5XQEMnuSaAcrQsWBypggmX7g1UzeohXTOeekmjfxBcxEdIJYh4y5SKlXgQMPejIRLGHbz7vBiqmFWrMEwKFLhr03s0KyED9gVUsCb4zuyuxNAT/vrXv4IDimsZ6AlcMgV2pYrYhfC+AQSePRMEBOD5TBoIQI3xBBDMpxp5PvroI/3ai62FK3d5NB599FGQ148GVjak4tGgmeKsAtDGjBlDLvVjKSw2jL7jJq0nZwzj1Gd2Czoo/grxY6GLiicA+uozn/oxIIC/8htvvBFPdi/SwEROByLWZWlRL6pTZdLjOdxdb7XRaHDEcPPmzT0CP7Y6uNRb82t6BbMeTxmKNRCnkzdoWGtJsSV38S71WhZJXl1J8FimxZm0Wo8Gc1unechFZVVR2fin+58PAyzG8yjSUdTBddA2nMU7hIeHc4izLvCCBQswsVEOZh3e0rpAXla8GVgvYxilI5MfwNDDqxvPYwK6djBhnRTZeIHryKwHMO1hQuKB52WoS8PDQyHAYjYTCh2f5ABedewhp1LdXoQ5hUYdP+K002VFPJZNMcmzKGmec8ZLXm3Y4pk0x3FZqSizeRnMYozHWxXjjsqLGV6vwd99993mbvzMFu5Mr6CGm/gKsWYJ3hPYLvBM5Dg2ZxaXY1xnOy8KpLuwOcvUvEqVKnwOICuu5Ky1UQJ+NGaxhPGvnzBhQqps8BHRww0EkXhzmqLyVkd+t8YOnACndxjoWujrXhh6I+oYZyTLL1aTMc10d4ssJgWNAAEmIIAPT8UpodfJWBZHntKlS5tCEmZdO0NbQfyyYfa1cGbUTL3J/JKbf+d0Fo5sz6F/wNlmk7C0F804bWU3L5nDM+xil4xZFGF2jVBFxO2pZvZUhbGU0z8smRnLMGrICpPiNol/memwpqpgdw7m/1QpG6NevF4xhGn7C34MLBnHSJ/ZWyY3MXlx+otktkAv0vNW1qMn3SWY+Wa9Lvo//YERmS6WAI9G8vc5+n1OZwJEmLE94wXeDxj2sRARQ+/hzcbEm4Vn59qzlZ1nmB9As91J32LeziidxmByxzxOx/swgPDozgsNHDCK49uhhEQFGIrlGy6VOhkKr1wTyMIRnRRFeubLelKAJyE8qA2fGZaW/AQgoCqFVc0tQadb+H/f2mBblV5U4blFa0tUmh7EzEgQphAVAwJOvwEzcQrDqEO3V68r2BnTh5KWeS4TTwSjNeNpQV7V+pPUzOB433PMP9l5FsAWfBiWgnAKHo2sE21KSnj77bcxVJs9g5lwbF9NRljWPgOyY09J08ODmG1hnza3sCs0sKBHXJHVzURX40PE1gNJXlfeurqWVAUwxpm9gsNw9XoWHcC8pcJgiEUvVdK6VS+7iNhaYDERI98Mp6IQMetLTlh4uDgezy3xEisnbeZ0TvXAzpp1R/sgJc8qMxcO0rfaAHMyBj9nyWkUw7KdHuxo7TDqx1jmByWdUgfYb5kc7wSvsbW4CQVZMVBeSBG5iQR4z3ktVRLKZ8jD61k3qAqwnS32wVj6s1Q6I+Mj1j3Mz5QkQfiIVaQxN6EPoyfrhA3crzHrWqqqMapGXwWYG3KSp5UyHS85s8VaOeL9GW03Cd46uBebULDcydHdvrWyZbZFnNyEspwbQTnB5iYUZJ5LU3JCudm+9PNop9TyDmN1z0xMz8FFO7OYe5Q+vbkJUFhSZbuziS/OL/jU00e5y7Zkwvq4TpWMAReua4mdneBRM2SxWHw1OefPBAFLAYrzw7JL4RgjmLJxSU/VybCjgwNeuVms3VfZUQelrD1cipsYGHKLnz6Ug121XFpHSvhKnQSEYQ6LUub+eQwXNL1p1H/11VeJsUxpfBeHDUOMvxKo1Issac9NgMKcmTUs9hDop44AdnHmzJZ1nPEFg1XSewFlasvkwQME7A56AUuhwbgSHKwNtFiIMVQlcz04yeCo86d1f8CF1aRg7ZvDqRtJFixp1XFUi7XWxqY2DLIcVEJ/MFmJsRJvsgEDBrACkDTx4qnof7sBdEOmXYCBEnDzcsDyohdirPUapRSDKXaNpp2C8QjMSgog4DyJMxSrbPCvysUSjF6FUTGcpIOVjZmdZTqNp5Y0TcOkHgNcmgqfmNi8ezgVC+MR4yBVAmxFDyEML5hlcoQAXnI+7AxB4CYFNGcqsb2Wr1yYuBPGnsIQVz2rPmwAS9osXqIg5id4CvsR5hXTBYZX5R/+8AfKZ6cuiwBZrMjn2Rs1aoSbiCZopGVZk7cX5wf5XHIXxXP2douVdF3OlPpWKgPxDK7SIo3zWG5gxS7IWbr6K+yB/CpctNbBV8Dc1oMJJlrKQMYrJ4kaNWow2VffWGanhdI0DHM6NGWab03wFdGwpwJMtNs333rxZwfInkpe9KZuJs+cM6l+OPtgamHlzpuqfF0qZISHgRorYWjThzf7WmiXhOPkDeU/iUsqLyT9HLpUfPoVw7Y7nghMTvzlw2tgYpnkfKhScOZ0jEv58QbA/GRtPfMh7skRiX05asWK92fjxo2TU6kfasE7xJzQ0TGUVBhfIg4l/CCzpzJg7uChwLRkLtR6WmPWCw/OuAkPuohehVnHSEpIdwQ4iRyOxgaHNwlrBemuTsLypxExoWNwuInhEr+IzcaZeyqelQs6aMQ0EhkkBNjAYXYGFgfUmJoNaBw6ytdBgqRsYrqwHmJ6EiRWiKe5gsNNnJ6B70ZEsPS567if4eIRMY1EBgkBfAuj7U3lc4EMo4KkbGK6sHPAPHg6sUI8zRUcbsJ7AHqKCNbQoUNVPDs/xSwaEaKARfL5HzZRmkpxciMHkJsxEvY5AsHhphhAm984iZFMbgUGAVaj2KZjqsPJeczmzJiQhzn/G195P4MQCm7ycwOIbK4jgPeAdoaGoXDzUVWwKZ+tzq5XlxYFgonlGY+LE86ofhZeuMnPrSOyZRoBNhXicMuJnSonJ1Vw9DvHGPEjhk0bEb9cnelq0i0D+3j5rqIpNdsGrKGledcP4YBwE9+244g4PwAqMqQWAT76wHdiTBk4oQG/HryfGUBdfPHF1p5wM2WAwxzIYVlacUhm2drPKgeEmzB86kNa/Qy3yOY1AuykM48H0dXh/qZOuapbty4fTSEeJ8xwbhjQmPg8EBBuwrOOvb4+x1rESwICeFdyDAM7e7UPAR65XDJwUB5PjKHwfkISuKlnz55JEEmqSAyB4OxZiaG/3r7AqYB8yIBT1mIkDtKtcePGRTwNOkg6OnVhpxg/vhvGBibu8sF085vpxODaw4Y7vDFN/0xnORKTWgRCwU1qTxlA0yM57i+1iCezdoYG/JJZo3/qsk5WMwXjnC9+ZoyEfYhAKDquXj3lQdXbPn3YGK6LxHdAb731VteLlQIFgSQgEApuSgKOUoUgIAi4i4Bwk7t4SmmCgCDgDgLCTe7gKKUIAoKAuwiEgps47s9d1KQ0QUAQ8BqBUHATx/t7jaOULwgIAu4iEApuatOmjbuoSWmCgCDgNQKh4CavQZTyBQFBwHUEhJtch1QKFAQEARcQEG5yAUQpQhDwOQJ9+vT5/PPPfS6kJZ5wkwWIXAoCAUSAUzr0rtJ0UU+4KV1aSuQUBMKFgHBTuNpbtBUE0gUB4aZ0aSmRUxBIHAHsTXzkOfH8qcgp3JQK1KVOQSC5CFxyySVpdyyMcFNy+4jUJggkHYH8+fP7/BO+ESERbooIi0QKAsFB4J577rn55pvTTp9QcFOvXr3SrmFEYEEg5AiEgpv4hGnIm1nUFwTSDoFQcFPatYoILAi4iADncEyfPj1igZii3nrrrYi3Uh4ZNG4C682bN1uwckY48bly5cqTJ491KySX1odGQqK1qKkQ2Ldv36FDhyKiwZdmLrzwwoi3Uh4ZnO+slC5dmu+OTZgwwflRaZ7MlStX8vnpP//5zylHPMkClC9fHlj4QFuoPuKQZJD9WR0tfvnll6sPYRUrVswS8oILLqBj5MuXz4r3z2U2Jbp/BBJJBAFBQBAAgaDN6aRRBQFBIBgICDcFox1FC0EgaAgEx94UtJZJqT6//vrrqVOnEIGvjebMmTOlskjlIUVAxk0hbfhoav/4449btmy54YYbzjn9u+aaa7g8fPhwtPQSLwh4hIBwk0fApmux7777LovKixYtYtzEb8WKFVw+8MAD3333XbqqJHKnJwKBmtNNmTJlzZo1ERsCDwPZuRIRGTNy165dU6dONWNUeNy4ccePH3/hhRf8vOTsFFtiFAK8aebPn2+h0aFDh5o1a1qR/rrEhyAAPyipXbt2559/fjRwCxUqNGrUqABo6qkKeIEpAAcMGPDe6d/AgQM1pHv37vW0dincCwR4NKpXr64bUQfq1q37zTffeFGjW2X+zq2CUlvO7NmzNejRAnhg8uZPrZxJqF3NxWL8jSED3ITDHr85c+aoZAsXLtR4VqlS5eTJkzGyyy2/IQD7lClTRregFeBdftlll9FV/Ca2kicIc7qdO3eaX8csXrw4q0tmM+zfvx9tsfKSkrlJ7ty5zbsBC7ds2XLBggXRlOrdu3e/fv24CwGVKFHCSoYb8dGjR4mMuDYHgFZ6ufQzAt9++23lypWPHTumhCxSpIhuVj5t8PPPP391+levXr1PPvnEj4r4kzIzJRUIa2R5D/AIWdkbNWqkEwwZMoTHz0oQpMvGjRtrZWME2FrIKClDxfW4qXbt2uzJyjC9JPAJAmvXri1cuLDqAOedd179+vXZZ6ple/XVV/WbibWOjRs36lv+CaT9nI5ZxmOPPaYfwlWrVjnBtZbAt2/f7kwTmJiXXnqJjc0akBiBUqVKzZo1K4bivF31AsLHH38cI6Xc8hsC1157rW76Rx55xCkeO0+LFi2q0rCxjgVZZ5rUxpwx99HKpFEAbho8eLASuEuXLmxtdQp/9tln9+/f3xkfyJg777wTw7+l2nPPPadR0rewbb/++uv60hk4cuTImDFjnPESk0YI1KhRo23btk6BeVhKliyp4nmjx7ADOPMmJyYI9iaN1HXXXYexSV/qAKej8OrgOAi16nTjjTcyndFzb50sMIEZM2bs2bOnefPmSqNXXnmla9eumDz13JZ+6TxJxlK/devWWFJVJCYqDsO3EsilbxEYNmwYczrEw3Vm8uTJ0U5BmTRpUp06dbRBym/qBIebIKAYdMNZ7hUrVlTor1u3jsGq31rCRXngEW1NoNiqVasqZOiIqhZcvmNXd9NNNzHdg84gdEadfD4oznli7GLlbnIQ+PrrrxnzUhfLPtGIibv4N9Gswk1eNQpmPFX0rbfeqkcKXlUWgnIxeDNcYroHMaHu1Vdfrc3hIdA+dCpOmzbtqquu8qfaaT9uevnll9UgiINHQbl79+7+BDqFUs2dO3fbtm2mANFOQTx48OBDDz3EIo5KzIipSZMmhJkC4wtjjsXM0iScvgiwcdK3wqc9Nw0fPvzFF18M9hwti71n0KBBcZbACqYmJrLgJsbUgC9BzJs3j4khOMuelTiRTLtkNDE7Ky666CL/SJ723OQfKNNOEtY0WSLQYuMxwJep9SUB3PNwB1Mxq1ev5thpOdvXxCdI4QoVKuCc6SuNgsBN2GuVcYS/DKDweHZC/Nlnn91///0qfvHixTGs5s68gYzBCLp8+XJcnLR2nNnEWrK+tAKAvHTp0mjwWonl0j8I4GRD20WUp2HDhmobAHfxII+4xh0xY3Ii056bYJlNmzZxMDt44dqDywYWcWvPyoYNG9S+IdKQoFy5chH5KzmIJ78WFpJZr/ntt984ZkDXvmTJEpOYiMeNGG7CwUKnMQN8KQgPPQtYM4GE/YMAIyD8/lmAY8sEviAsuUaUTW3e5hZT9YIFC0ZMk8pI3oTp/jP3rADlO++8Y2rEMpP+9BPT6Xg2apjZ0zS8e/du1atwvfviiy/Qgt1VLVq00AYFnCrSVDUROx4EtF84b+WIW1KYpLOXRXWSbt26xVNmktNkTyUvulQ3lH/LLbfowjp16oSPpf61b99ee3B07tyZl79OGYZAz549MWOjKaOkmTNn4mmhtGYGx+6WMCAQTh3xtuX1g+5w0MSJEy0QeGGTgK3vxDOsbtWqlZXAF5dJ5kKPqmOY0LFjx9iA8iZZv369RwL4qlhMDKy5KDQ4WsDc5Pmvf/2LY3bVLTh95MiRvpJchHERASYQynbB+U28vM0fo2nVB/DC1efhuFi1K0Wl/V5fjcKBAwei2UqYTsNKWFt04mAHGjRoYBrUWILBU1wtFKA4G1Y0iUNPcuReUDvDiRMnMrQPsrXFt+oHh5uAmLOZWPZmbwqbXdnCwjZrfqzQ/fLLL75tANcF4/wmk5gUDYGG5iZQYgip07CYwMvTHFu5LpIUmCoEeBwwhEfcosRCLY9GqgSLp960X6fTQwACPIH88IFmPxEbXOP3OTQLSfcw1EPDx9ACiDhAmn2/H3zwAckwPMHmwT5vLwYawb4FK7FsjdPs2LFjLU0xyNaqVcuK9NWlfHPcV83hgjCjR4/GoxJHSg5j1ceB4uEyYsQIPVaiGgb82uGrR48ecsyAC9BLEa4iINzkKpz+KGz8+PHsNSlbtmzEg3v8IaNIIQhkgIBwUwYAyW1BQBBICQJB8G9KCXBSqSAgCHiKgHCTp/BK4YKAIJAgAsJNCQIn2QQBQcBTBISbPIVXChcEBIEEERBuShA4ySYICAKeIiDc5Cm8UrggIAgkiIBwU4LASTZBQBDwFAHhJk/hlcIFAUEgQQSEmxIETrIJAoKApwgIN3kKrxQuCAgCCSIg3JQgcJJNEBAEPEVAuMlTeKVwQUAQSBAB4aYEgZNsgoAg4CkCwk2ewiuFCwKCQIIICDclCJxkEwQEAU8REG7yFF4pXBAQBBJEQLgpQeAkmyAgCHiKgHCTp/BK4YKAIJAgAsJNCQIn2QQBQcBTBISbPIVXChcEBIEEERBuShA4ySYICAKeIiDc5Cm8UrggIAgkiIBwU4LASTZBQBDwFAHhJk/hlcIFAUEgQQSEmxIETrIJAoKApwgIN3kKrxQuCAgCCSIg3JQgcJJNEBAEPEVAuMlTeKVwQUAQSBAB4aYEgZNsgoAg4CkCwk2ewiuFCwKCQIIICDclCJxkEwQEAU8REG7yFF4pXBAQBBJEQLgpQeAkmyAgCHiKgHCTp/BK4YKAIJAgAsJNCQIn2QQBQcBTBISbPIVXChcEBIEEERBuShA4ySYICAKeIiDc5Cm8UrggIAgkiIBwU4LASTZBQBDwFAHhJk/hlcIFAUEgQQSEmxIETrIJAoKApwgIN3kKrxSeBgh8+OGHS5cuTQNBQybiWSHTV9RNGQIjR4587733pk2bVqRIkYhCvPrqqxMmTNC3mjRp0r9//4cffnjZsmU6snfv3u3bt9eXb7755ksvvaQvCfzp9E/HzJs3b9CgQfqyevXqL774or5UgTVr1owdO7Z06dIzZ84sWLCgdVcuU4bAv+UnCHiMwMmTJ2GEs88+m15evnx5Lp0VwgvnnHOO+RjkyZOnePHiuXLlMiPz5cu3ePFisu/atYu7efPmNe8SfvLJJ3Xhq1evzp8/v5ngyiuv1Hd1YOjQoSrNN998oyMlkHIEZE5ndl0Ju4/Ab7/9xujm7rvvPnr0KMS0atWq7Nkj9Do4yOKmY8eOHThw4MSJE8h01llnVa1atVSpUj/99NOvv/5KTI4cOYoVK2ZyEyWQhkilAykZYR05ciRDlYoWLVqoUCGSVa5ceevWrRmmlwRJQiDl7CgCBBuBvXv36q68cOHCGMq+/fbbHf7/17hxY52refPmt99+Oxnff/997q9fv14XMn369IoVK6qUF154oY5Xga+++or0zONUgty5cw8YMMBKoy7vvPNOlQb6U+OyiMkkMpkI/C6ZlUldIUQgfm4ywcEGpLlp27Zt5i0rDG1F4yaV8vHHH1cJGFJZefWl5iZStm7dWsdLIIUIRBhd6z4hAUEgiwjQs3v06KEKIVCjRo0sFpjZ7IzUGI6RK1u2bK+99lq07D179rzooovU3RUrVkyaNClaSolPGgLCTUmDOowVXXfddbNnz1aa16xZE8uO6yg888wztWrVotgdO3ZAMVb5mMw3b95MJI4CLVq0sO7qS0rQsjHQ27Rpk74lgVQhINyUKuRDUa9a+fJUVWZqZcqUwb6OjXzfvn1mXcR8//33KqZcuXIMncy7VpgRlrajW7fkMiUICDelBHap1E0EGJqphbb9+/dv375dF/3FF1/06dOHS4ZFyoNB33IGcubM6YyUmBQiINyUQvClapcRwL379ddfdxY6cODAc8891xkfLYZy4LVodyU+OQgINyUH57DX0rBhw0aNGiUTBdyp8MNMrMYFCxasXbs2sbySyy0EhJvcQlLKiYUAXpHVqlWLlSJr9+bOnWuZkzA2sT8ma6VK7lQiINyUSvSDXTdu2Tt37kyOjiwCqoqGDBkyceJEtsVUqVJFxTz66KPNmjVLjhhSi4sICDe5CKYUdQYCv/zyy6lTp86I8uyCdTrl/338+PGuXbtiLdK7VXAHZ8uLZzVLwV4hINzkFbJSbjIRYDPd5MmTdY1Tp05V2+4qVapUu3ZtHS+BNEJAuCmNGivNRO3evXvhwoWTJjTHEnTp0kVV98QTTzCAInzFFVe0bNkyszLccMMNwmiZBc319DLWdR1SKfC/CLRr165fv37K+xGzdIMGDdq2bRsDnfHjx7/xxhsqgZ6Rcdm5c2ftnYTnN8VGLAQebNOmDcamiHczFVmvXj1PLfeZEia0iYWbQtv0SVWcvSP4iMeoEv9Jzo3jYBNnGvNsOY5Y4aiAq666ypmMGKxOnJ2CIVzdhV9GjRoVMaVE+h8BmdP5v43SWMLzzjvPWtqPpgynNUUkJjN9gQIFmLjBPmakGW7VqtWwYcM4f45IeKpChQrKX9xMEy28e/duTWrR0kh8MhGQcVMy0Q5dXR999BHTMXU+XGzl8dtmRhY7TdOmTe+4444YaeDBvn37QjGcwUS9nGkXI7F56/PPP+ekJzX95HBe7X9gppFwshFI4fksUnXgEcCH4JFHHlF9mvO/OSrAnyrL+U0+bBeZ0yX7ZRCq+tRARqnMqZVff/11qNQXZbOCgHBTVtCTvBkjwLlIevUt49SpSMGZc+r8Obw0cdocM2ZMKqSQOm0ExN5kIyLX7iKATzYWcXfLdLe0Q4cOHT58mDIxtGNpitN4764MUpoTARk3OTGRGJcR4KNv+Ge7XKgHxXH2gBCTB7gmWKSMmxIETrLFj8DFF1/MvIk9JRxQGX+upKW89NJLWd2jOu3hmbSqpaIYCGTDPh/jttwSBAQBQSAlCMicLiWwS6WCgCCQAQLCTRkAJLcFAUEgJQj8HxI/TxC9IBG/AAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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jLMbubFEjXNO01X3S9Nqzn3LKKcVe0PfyBkgosPfHBop3+5YWnyuFnGNcaCeY2yWYSkIqtsA5MxIVQSCsgCoyEDUlQ9SZVvOvWZtpuIvQYry9c/ZDsVpGn1aWF110UTZnZ7xgYkllyLpZvjg1WoGmDdU+GJyjUMV2Tr3l7VUK3U4lkCS59DBiPzSFoD+ZzutIp3N88YtfzC2KKZbc01rFbc8lI9F/BIIB9B/zYWrR2VNW5WLKmnxOOOEE6bwDYIs44Z6WGABRx3bbbUemlLmCHUA+vBZXSKIFUnFbjRwFObdebxEQhzoHuDupUSf/53/+x0GMaUGte5dccgltsD0R9ig67gUXXDDhw78yspNJ7LbbbvZrdEHFSsz+vELM/uuttx5rgSy2KpaJ9EAQEEGWdG4gTUejw4GAOdflhAbdSUIepxA2ds0EJVK9ycothj3ZbahY0l1llMyx690VvHKZZZaRIO3hUprLX3311UQO6Wd6h0mYU2zwxhmmxgzALM/ORzwf4LL4zKIePWeBK9qG9bW0iEvUv/hwBmiwCQyciiJ5ApNcMQ1Ky//BUhWtlxDgJOh7K2XGz0BgvAgU5fusP0UfuPfee1mIfvKTn0xVec1IaRIDsFf4yEc+Yl3IwyvJBlIZfmQHHHCAkp7NBCy++OIsX5xBW/IGSHbwqRgOYWLcaKONeB3nB4uJujIAkzvVroh6OoOvpiO9UscwPeCmuAvsZE2vs802W7HPg0rz+bILMYSYVmIAmRJDK5OGIOdEYrAIOC2L2Qbt/RZbbDFYSqL1WiMg5pj1qNXquuuua2pKwRu4BLka+2USEKoAz1CM1D4X+Pa3v52WI3zRc2ZKYBhJriDmzeWXXy6TlRGpeGrIhEMNUHqk+LOWDMDsb+2P++nJ/PPPb2LNChBCLrM/0ZBbmK0yFZn90SM6R1oOkFllaZV9XFoUbL311vlwguIIRXogCDgz3bukaR8wEeJAaIhGa4SAAO/M+RoJFgiSaaZ8K3FyGB4ATzzxBFvEpZZaqrFwzmGvmNPk2Gk2l8OLmLg43aLsPfnkk/kYE3IQHDlgygzj1rTTTkse3qlxedYG1CiRT1OxZBYRKVNOA0zaldCxxMZj860qJJDnyHjk2Y6JDJFJ+vznPy9zxhlnpM/ImZEYLALpRDDjErGABjsQdWndtG4Wzs6DEi5SCmEbXBdeeGH65NMENS4lsNOH0pxGksOoPSuBrSY14f0sGTcWYwExfXQmhKtkHplRreUOIMHhLzsNy+f8k/PXFVdc4Sddh2hrVYu1QEHNFVAEKCsCr0tWSmMA1v5MhjFzG7rcnUgEAoFAXRAQOISe1rGygs+jmdUJjS63JH6p3eqCfUMWdajTZEK7SVCZLF9oEdK5h8nDIDX62CsXGlrtYmvMADDAFNohdVXIPeJ1nI30VpDntBvqFvTdqkcECDZbjgHwNwUEVfNUU03VqJ3vVotRTyAQCPQHAd5YjE2Kzl+t2iXVefTRR4luWhVozJ999tnNbMV803o+31C+QKHMTImXi2XwjFVWWUXcIQGii/k5XWMGkPuQEjqJGVpWU6nncNClMtX5SaVDNZRYN6oSA6D/oSky0tWhMygJBAKBbiHgk0+63FtuucVpJR0yALMEM3fSf8Ee2lBit8H1DPuhMMgmkXiGKBFtnhqeWECUvYyruNRWf/Y3HjwAOW3ngaG+xwOIgBh15cxIDBwBdtx0MwMnIwgYDgQI4tnzjLcv1vXCSLSf/VOd/L84CuRgM500VGMGQNqTPZ5TVynZiVM66fYAyxgeyglrAZbmmYxspatH+pXzIzFYBJhRF8/fGCwx0XrdEfDJswQZby+IevJhMjTJtgLjrUF5QieSZwrI0rO1ZAB030m10qmpU6nTA/0pMniK4u1twAYSLfQZKZwRk9ZgAAMdn2g8EKguAhgAmyL05RmjSCvuwv9JdJycSTO89957Ozpmww03ZCfKJlWapCRPMrXUAZCSk598+tOfzv2sY+Lggw/mv8adD/Hkd86GZCJmwDh2F02b6ti1oDkQCATaIMBkqKi/bVOy1S36TiZAzH6KRp/OHjjiiCPy5O5ZClF/k+JBgqJY0AimhmJFcBeQU0sGgPslrSmfN3afa665pp7U6OKbJy40dk1pwy4oqYJ5tIkLxKkPo06ObDXq0bCS+tBDDznO06JpWDsY/eobAiYrjj6pOc5cncj029NmDnSyoSg4qZhFpMPiRYxo/5S75s8sc64lA9AHAhNSIPH2GFPWjgHQ0RP+YAN2c8IEJgbAJMBuoBQtbsyxjAI9RcDh78yLgwH0FOThrtwKPQWqIb1h5dHdzjLuzKFKxA6aQMSzWuoAgMjnK8HqE3366ae7C2uva6PDSLaeNgHFMH6pXbszneo1DVF/IBAI9AEB87LvnVw3z/5sOpnnd6VpoRA4w6ZrArM/GurKAOxirKDtepyvQpzSFTT7WQl/BfE6iORoAnK766yzjhG98847S94cuUAkAoFAoF4IMCMmiLfR5yaWKGcEmO30x9sXho5OPVGb80XMgeN9vLF8XUVAerLSSiuBQ2A4QbtoObDZxu5VNsdEv+iii5aMgsWHopmxoRE1kDa4GA+2sh0JwmqKgDWpmMM1Jb5eZDuJxWWFzsrDRz0xO87UZXJvUSO72P06TZqtur3NNtuIkdTqbsXzTfeNvuP77rtvDnhXcfqDvJoiQCRNhVZT4utINhMPEZ5FA8UMqkN/jRmAHVCOk1cdQDunhCUAxa+D4ugzxIZLD9Ljp62MuKFFc67Oq42SgUAgUE0EhAutmoV3jRmAMc7HpxWNYas59o1UUQQ5wwEbswl4/vnnUwFcQUQgaefYND4SOYFAIBAIdBGBejOABAQFwISVKl2EcgJVCfzAmHfnnXfOGnzLf/FnJlBVPBIIBAKBwHgRqDcDcJzWpz71KX12bNNpp5023s4PvDwlNp+v9ddfv5ESZwYceuihjfmRM0AE8Ga+PK6mp3sPkLCmTfM1cWgUa2mXk0malonMEUegxlZARk6MpBVWWIFqhRKY02btxtLy3+mdRbJ5BggULse2hi1Q8VakB44AG4zVV1994GR0QgC/U2ej3njjjSm+bLcMzztpOsrUCIF67wAALVij419qhHh7Uhn5psMILTaLLgLtn4q7gUARAYbRW265pZ1Kmv2dCJg2ysUykQ4EIFB7BqAPjCZ5hNkHOBuzvoNqw37cccexa2L8s8MOOzghoCuOHvUFZLCUsy3u8LyOwdJZbN2b41RC6yE+hg4ZdcvZGDYBVhXbb799sWSkA4GEQL1FQKkP7Gq5VziN08KnpuNK8uNwZ4cDSzjazJkB73//+2val+Eg22naKQi7sBy1iAfnhKn77ruPSTEXQkPA09Ds72TapmejYhUKD8dIRS8mhYBXoe6XtXMKbWrPW9O+EPen+KbGkrtgTXsxZGQLsZI+LaETzzvvvCr3zkF4RSm/NYQQKW0IZjExxRRTpN4tvfTSbUrGreFG4FXD0b1LLrnE22zJI5Ja7XokIhBDoCTw4SZuyVm7LgwlwcwKjjzyyDRLUskI3fHggw9Wraf77bcfwsSXRycb4o033lj40lZ0Mi1zHpHyRKbKC6nL6URM+ap1KujpGwJDwgCYgYqn750WXr9v2HWloV//+td59ifLYrfXlWqjkq4gIM5gURNgo+lova7UPPlKrBsoirIvpJd/k002sRtuVbPgIun0ISVdWJoziBibtSof+aOAwJAwAEPlKByvNd/aeg2b2CCvfI+vMvvbyNeL+FGg9pZbbjnooIOyQt4uk0qgCh0XCz69Of46XURkNzY/rQjjbV481cARFOnw0VblI39EEBg2BsBS22mRtRg8cUwdZcOXzQdsC28TUwuyR5BImnlyku222y7paaaffnrRvJ1ISt7oVp8BEVZ+7rnnRkAKFiu4vEOEnnnmmaZk/OpXv3JilMKzzTZb4haOudcXlbTZKzStKjKHEoHhYQBCK6fJ1MHHtRiqL37xi+mb9JfcthY0jzKRpCW77bZbcR1tW3DKKaf0ExNnRcw000z5tXGskGNgWxGAMZD1572LcCMipjgxvFX5yB9BBIaHARg8hx37NmrBACgYs5HJuuuu++yzz47gy1fHLj/22GOMdPMUzFR08803d6xbH/pC/TDPPPPkpkUKMcW3atdRqXnV75F3vetdmEerwpE/sggMIQPg9FjlZQ5nhdVWW+3tb3+7z9L0YUJhmzGy718dOy6MvuizLCnnmGOONMlOM800Zthtt92WorW7QiGCGlZhwgWqnwraa6NRbzgC2PuX0HvxxRcRwBVAYTpeL5gtgvWQwq1kRKUa4ueoITBUDIDHIx2A9769EfQAx5jUuBhMhtFed+eLAXZtNJvGzouRSMhbBPCgN+4WGuLF5qPu+Ka1EfhoMdlBeP/Ttcoqq7RRC3eLwqin1ggMFQMwmaaFD2NnEfZLA0PqQtiSrgsvvNDaKl/9MYZzBpNjLNPH6avecMMNTR8lIuNn7RBguXvxxRfzvTKmSeDOcpRQyNs1sffKU56lFvKu0vF6YVQrIdJJU3DSa8wKSHBZJZHBDRhJTz31VNPykRkIZASGigHoFamoD4aV2z333JM7mRLWZWny9Zdvpw11vsROufeVi0Cm9FS3ftqDWyqmCYLLgp+NLKpbbUU9/UeAXIgih6wmGec4AdzbRVYj4kLnmzxTufLsizybfLW8yXRFJ598svqbdop4h3ZXeXtfzr1OlEOG4LhNC0dmIFBCYNgYgGk8zfKLLbZYqatWanvssceuu+7qo8qcoJRgjG8LbzFVenaSP1lhZ4cdnvcOgJxkhfF4NRGweHeWZ97nebuw/GOPPbZDatkUlV5OQdxwhaaPMyP2rnrP8ztMRtS0ZGQGAq0QGDYG4KsQCNcnwd+9UUsGBcsxpy1ec801CyywgEVTvoofnpWU+dpn7PI9E93k64UXXmgFZat8K33G1+kr1dz+++/fqmTkDwcCVuWOqTDWyW+Aqv/EE0/0CrXv3bnnnpsEmJ7yrGiA3lLvc+kpPCa9jYI8p5fK6+pUCYVvvfXWUuH4GQi0R2DYGIDeHnLIIT4MklBhUtp03rz8l8J19NFHM/Eumtmlr8vXOFXhssjymaWrk6A95oJsOY4n2chPgIW06UXcqiYC5mgv19lnn00440UiFyLSufrqq9ts/tjwbLXVVt4W8Xk8K9JDY9ccFnTllVfiKF5Jb7ia3/ve95JbjsldGquKnEAAAkPIAMzOTOV8G1TB45Xpi6m7zz77UOil2b/9X7tvRxEI4t/mTbKByJW0N+FoU0ncqi8Cwog6WiC/A/aCJvGJdYcVwxJLLJGrombwrjbquiZWeTw1mgi8WrfzKzU0CYb2Vlu6Y0Wf7CjEhyjGy3WLHrgo9in2/f7777f+kmPBvsEGGxRvSZMsWXDlTKYXZvlddtkl5+SEj1NsZwcVaGjrrbcmsU2avVwgEqOAgNic6XQH63qfGxMAXlpeS3KeTrpPaOlt5CxCLCmEg0fsSr11AseWzhPtpLYoEwj8BwLDx/essAhb/qOTr3wztuHFi6ZX9Igxl2M25qXrW9/6FgbjojFOrTDbaITxjjvuSNO9rboCrVR5jQ9GzpAhQGrvFTKJM+sUS8474z0UubMT928PkmSm9za9bPa1gtPJV+2QARXd6T8CQygCSjqAEgNo9dOWXIAXXp0TgJ7F3pdfuSzoGh/HIVKjHER9ro0FImcEEbAsSDzAu7HRRhuN+WIUA5F6xIYyfEdG8LXpXZeHjQGQzzhW16dim+wwvDPPPPOBBx5wzLp06bKqShO0v8SpNuYughoB+ovXBOI0/Pa3vxWCUZ1oYBge3pi9e33rWLNoDYK/0uKyEGXJ42X7zW9+U1rOm+XlH3DAAc5s8X7S+np711tvvU42DXXEJGgeFAJDxQB8RY5w8l25rOuLH5V06XIA78deuZLtXWYG6fH8l1WGg1U7P2ySxFZkmPR4iYZBjXG0WzUEvIrMB9Ir51WhDGAvlI7xEsDZ+8bYTH4qwK3XCe/p7a1aR4KeuiMwVEpgcnYGPOl8XWuoDjWul156qYWVQyVZUmc2UEpYr4nbkzO32GILR3Dkn8UEoyDzPlvPnXba6bDDDssnrxbLRDoQYCBAHGSDSAqU0KC4confQDWV8dl77715Dorqk3MiEQh0EYHhYQBcZkjbrZ7IXr761a/aLydD6Q7BYvBD2uOwp2KYfs8KLEqkU6pEUEa7cpm+z2IssIsuusjKziaAUZB6mGqUHoyfgUBGwOKRQUGyIOAv4me6ZUvKRojFJ22W5X+sITJikeg6AsPDAAh/hHkAkPi349IDt8dUFHWOObkM2zvGfPlnY8LOnXCpaP7fWCZyAoEiAhRXlAEO+JVpxrd8EZyqWCDSgUCPEBgSBmAB5XhFZjnis/uQ8lkrXUeNH6Y9wQknnGCvoPK8assNMfm3YyjpFfLdSAQCTRGgB3bkJDmk6CNtRJFNn43MQGDCCLxuwk9W50H6sRVXXFFgBqtvh8L3bvbX5XToGBPP5AvGToN/r3zeXmRQEs7iaOVf5m5cgUBTBBj5MELDAJrejcxAoEcIDAMDYCMhujKAWEmLqNUjpIrVEtG65FD5pnwHdwjcyNmHAWiHyudihZEOBAKBQKD/CLwcT6ruF2N/Yhm9cDrroPSuzuFjKVR3JIP+QCAQGCkEas8A+Msw4DFmM8wwQ0jeR+rdjc4GAoHAJBGoNwNgv2/Vn6TwHOtz4OVJghKPBwKBQCAwCgjUngE4/529PzUAEXwVBsyZfGI/VIGSoCEQCAQCgfYI1FsJfPfdd+ues73EdKuI6lXQdo5g7UGPu4FAIBAIVAGBGu8AHLUhdhsQ+dxWZPavwogGDYFAIBAIdIhAXRmAYDviLjD+ES6xCr7yxFAcODsEPYoFAoFAIFAFBGrJAARj+MxnPnPFFVcI+8PuviLnIjmdtQojGjQEAoFAINAhArXUAQiZe9JJJ+mhwCmf/OQnO+xqFAsEAoFAIBAoIlDLHUAKzynwwznnnFPsTKQDgUAgEAgEOkegfgzg1ltvTbESHY4aUpfORzpKBgKBQCBQQqB+DODkk0925ItuiJ5YQeMfkem6GIy6NFrxMxAIBAKBLiJQMwbA9NMBXvq/ySabpMCcXcRiklU52M9BMQJERzjfSSIZjwcCgUB/EKgTAxBvmceviJsicbL8GVTct1YDQx61xx57tLob+YFAIBAIVA2BOjEAkX8cyAVBC23HuVcNyqAnEAgEAoF6IVAnBpBlPnPOOWe9UA5qA4FAIBCoIAK1YQCEP08//TQEHf2Y9gEVRDNICgQCgUCgRgjUhgE4Le+Pf/wjA1BWQBU0/klDvv766y+11FIOp4ywEDX6BoLUQGBkEagHA3DqC+9fgzTTTDNNP/30lR0tBwJPN910IlU8+uijlSUyCAsEAoFAICFQAwbA+EfUz6uvvnrqqacm/4mRCwQCgUAgEOgKAjWIBbTBBhs49UVvL7zwwpVWWqkr3Y5KAoFAIBAIBKq+A/jhD394zz33pHGae+65Y8ACgUAgEAgEuoVA5XYABOgOeMndu+666ygA/Nxwww2JgHJ+JAKBQCAQCAQmicCAGYDACWl+33LLLR9//HGdcZ7iT3/608ZerbzyyqL/N+ZXM8dJNY6sERyimuQFVYFAIBAIQGBgDOCZZ5658cYbn3/++W222UYANZyg/XjccccdH/3oR9/4xje2L1aRu8cff7xgFauttlpF6AkyAoFAIBBoRKBPDOAnP/nJYYcdVmz+qaee+s53vlPMyenFFlsMV8g/n3vuud133535/+te97rjjjsu51czsf32219//fUYWzXJC6oCgUAgEMgI9IoBiNj8+9//XjNnnnnmGWecQR6SRD254ZywqJ9hhhnSz09/+tPrrLOOHNb0uQAPAMzDITB4gL2C9Jve9KZ8t2qJD33oQyQ/wQCqNi5BTyAQCDQi0GUGcN9995HVaIbh5je+8Y3G9nIOn6l0lu8CCywgsn/Ob0xgD6rabLPNfvzjH5944olLL730xhtv3FgscgKBQCAQCATGhcBEGAB5fRLZm+Ut8IvtPfLIIw888EDOec1r/mlmOs888+yzzz45X8K0vswyyxRz2qSXXHLJs88+G8OwD3AKPB7jryMh2zwStyaMAM87Jy2nIS5VAva8XSvdqtTPX/ziF7vuuqvX75RTTmlqO7Dvvvs+9NBDaN5///3DvLhvY2cHf+ihh957773FFvfaa6/555/f51z6or2Be+6552OPPWbB19QD9LbbbjvmmGOOPvpoAQKKFUZ6HAi8PJeP53r44YcPPPBAAZldrUQxIvVb1y+88MJPPvnkb165yPHH00jzsj/60Y+cAq9vr33ta3fbbbfmhQady4rpbW97GyK//e1vD5qWcbdv6ud4scQSS7R6geaaay5z67jr7fsDefepL8wNGttffvnlUx+dMNp4dyA5aYXE1bHD1l988UWDlS8S1w4fHFSxK6+80qQxxRRTlN4u5t3y6fmYgBdpO+igg6j9FMYzivkpbV5xLoi78847b+PdyOkQgVd1WE6x+++/n/y9fSA235uBxJY7r7ZVSWs3EUBLd2Wmt8eewG6jdLcKP2vNAL71rW+VPk6RtzHyYuaiiy5qk1cFqFvRYJlZ1CGZ64VmKhWuGgOwVEpe7h0yAKfOWTgXx8UJGT5PhhWlnlbkp4P8xjThu+WWW4rUfuELX0gdbMoAqAbTjsGM5L0tPhjpzhHoVAREhWsj9uCDDxoSO+vXv/71hD+JAxffwne/+93dCtbvbbYiWGihhUz6M888c2pl1VVX9YXwDiMFEiLCZ2DtUCQg0hNGQLDV/fbbLz1OOvf5z39emsDHks30JH3CCSdgyXfffTenPJZO73jHOybcVj8fvOmmm7y33sx+NjretkyO0047bSdP2UxvtNFGdsO2YsXyX3/lolqbb775fJvVGR0vj1hexD4sI+zdCd+Y+RUp33vvve+66y451o7LLrusD794d8y0bav48OIEj1kyCjRBoENekby0PD/llFPamv35z39OxvsdPj6BYmuvvXYi11GLxccdCZnWSu5anBZvVSFd3x0A7XoCnAiLI1sjmH//+99nm222VObnP/95Y4GK5JR2AAgmq8TeiuRVbQdg8ZtkI2PuAKx+irLyOeaY4/2vXMWVkEVY46an2P1+pvGq9M74u9NOO7300kul1rG0vDlYbrnl8t0OdwCq/fjHP56fisS4EPinkjaP0JgJi3GaGV9U8S0c86kJFOBLlZ6y+bjssstyDTjQBRdckE4HwwxyfiQmiUAG08gCubE25q1Zq994t8o51p6nn356lSn83//9XzL9MSkk6yDq8YUrSfRhx3bRRRfRhbpslD/zmc+kGrjdHHnkkWPW1ucCArmvvvrqSaxfbJqW/hOf+EQxJ9L9Q6BDdpGdWvsmZOQ6QNtM1gQL+1mrHJwgU0uzZ9Nn4SBfpOheb0dyu2MmaroDgPZ///d/p9fuvPPOa9XNFJZVMRZZrcoMPL+4A5hqqqnSytqS5aSTTsq0VW0HQIVrGQvY9juAHXbYIY3RZz/72R/84Ae5OymBhVs1pxmW7O6GG24oFRjIz7QDsHQoifiLxBDjbL755rqGq5122mnpVt4BHHDAAUQOpYvDUIKC6IwmvFhbpDtHoNMdQJL+s+2hA0y49/qv79ZW45BDDmFTROP/ve99j8FGbtT7ffnll/uq5a+xxhrJ6SzfHWCCrNMmFwESmMEAKRlX074xSv70CCFyq2eZ66Vb6X1oVawi+STOJCFJsUGfRBnwu9/9riK0lcigVx/XQadUGiVJugrt21i1rrvuutJ0pBXpLEaVOtvmvbIJoL1QDCco6TZkUhuY5UtXlgr49iu43Uldrv7fjhiAQPw8e3Xmc5/7nGHoW6+sGnbZZZesmWT6WWzaSieJoUy4nISLtwaY9gWaaxBAtcVCeYCUjKtpIgirhnE9UovClhErrLBCsvQnOeRq3omkpf9ds2sxBJNv1yfjmnw93arBS0W1O8naknzMwBWv/LpKDHClZfNhVqwIr50AzmO/K4QD1tr2X/ZcyXc3NWNUnHzrVh6JCTTfySMkm1/+8petbn75y18yCsqmimZ/1g72+AiwgHXr5ptv7qTC7paBw7PPPmtvjoAiDT5pca3l2DOl8yy72+5ga6u4SL0IDtNk0q20VnCyNF/C4t0apal/0y6BLdx6663XivKDDz64aAXbqljt8vFy/ZpmmmnooiRs7wbYBazIvsTXTRnJbIkBxcsf/0IL2Rmn9d8AaRtX02MzANZllk4qBX0xIj+lk5BtXsRei1+oAXh1Hn744Zb8+C3Tz6yuXGWVVexOeIcxUHGLMiCJX8YFwWQKs0YVn452i4kkAlw4Ja9Fp8MTUqFTjgNtSJwp6+o79TRC1LhPbyxTnRxu5MlqwHdrQ1DTsWDIlFaaPsM2G/FZZpmlUdFanbGYACUkzz4oe2taQI7BX/rSlyQYB06gqq484i3ihIEeX3eykBalRtqFB+y4445daaU/lYzNAKyvXSVq5HBc9Nc6tw8COMs3UTbTSy8aQdEFhiqY1sg+AIUcRuiNS6T26KfZfNttt6W4s0EBQoJIJDiaRiwTW0IVHUZq3fshlrXyfeZPPeq7ak1Gvau86zWbEPMpQyxkjEXRNrHrzY23Qm/FscceO96nRqe8r8wHRdJCusX3KAX5IBxOu7r+40AobT2aPvlS66YCWklfOtvW0q2K/tSNNhfh2hFHHIF01p80sbmkdJqO2ef4lnJ+TxNsz7kgIMaewLRbtFV/4oknkuOSfSLjAUvvHlHCDYr+zZV0VpkYykYXWVCxXfsSmWZ/dtleXO/r7LPP7llRIhLPKBYebNrS0tI4vaMCNxUNrjJhynDATmVq4QdASmB7mug3FpZsjeatgw0FwQHK++CtSHOZv5y8MuClBNlCMpdk+9DGUsvSJPkE2OiUauj/T+95NhzwgjUNCaOMLyIFUHnf+96XhyxbAbX3BPZC9scPwEFVvC6Ml0lGowi23v/Zz372yqf/8h/zfo5KZB+m5Ne+9jVTaP9h77zFMUJBpA2O3vIAKlZqDZUmAh7exfxep0n53/Oe96Smwc2+O7fIPjVPylYHtmn5VlcSht+ivrT1JtvBeDqp/4orrth0002T4NILJFhNJ0/1sww9fxYd8LDT2eLn6v3mpJOQ/8AHPlBidf2kc8y2TChJCF5kAOkp4tpkWJw64u8AGYCpgaVApkSCQNVuoE0Hsxlo0aS1VJ6fdqqzCgwAbcXNVtMQXnhzxR3BCDx9Dub0PFizzjorlUwJeT8tkkoxLglI7AkaS1YkZwwGYKGnz2npWqR4UAwADbh9Xi7xBigupdmD5ls2jMVbReI7THs8XbQdwmAstdRSafg14SL6P+ecc+w8OqxNMR88xZFn1UNtoE5MRROd1zD5kvgiy1qN+uowJErRXCcGUJocue0wZKLvRWr2E0a/dU1+in7bt/FPpPrbFzSkdk2LKHTpQspMS+BGBsCioRQcNDMAu0aAqASr6+mgJJohL5ZJ5rhQZb5p8Z6BbZpgxZReQpptyDeWEaIjeWursJEB9LRfiZjUu/Q35WAA6Z1Huc0llVixjBVkxoF2txhCsfMdwCabbNIIxeRzEp0kDUS7xW/fF9TGp8H7Y2awUsy9JgCgLCxiMnnaulXDGAwgWf2bBYryHwe8pK30Vltt1TthS6se+shtnKlYDQkBlH0A2+FU2ObR95yioJjLlDn33HNZ6bSqqlW+mVqsYAFM1ODiiJCGf8YZZ/zgBz+I67gmUK3mTECepdFKqwlvifqpNIiJvB+t6OliPrKZzGoUB/KCEiYQaqX6EYA2qh3fYeqvvwY6r878JAJSprjxsgVW4GWYZpiBUQDc+rbn/e53v5vaTfsqUso8gfo+UdvIAPTxxhtvNA2lTuETufu092lrnwYFDr14t1HIlA7ZkC+CbCWBgDEHmuqlKKmDdt6lPf3002b/FKuR8MEwFenH7y0+mEuwyi9+y2O2OK4CaKCwTYPCQzM9a0+DGHNFGiaSk1Qg/c04GAv2HcWvoEMGoJ7E+MdF6piFhT32Rqncl5JGStd0UF/a79JSzUBW0tIkSSzo7VN/WU7iEGO23rcCYzAA0kmdtwbMBHnhsgkafXfO73PCyTD5+ykuY5GBP+db3rmzzjprXLRxF6JJztw7VeU1pXX4/ve/P66q2hSmiiTuzHSaZ/sgTLv99tsZ1BaX+Sb0xk0MkWuymcnkSQgEDYFGyY8ZbcUVV8wl4WZo2nS8W7dMFklqnJr2cVpa5sqTnxpzrKKiKN+VELDW6ptWIGeaIjHm3BEJjLk4H+WSE0tgvUzFiobUuS1y/07mlNSuhX/x5bR8PuqVa/HFF88VWnKWiDRzZdGE9WzjoJfKj/enRRgqijSURKOQ/NSnPpUpLCWIzpnwlRpNDMCGBmMr3fIz4+DZxruTzPGlpI0UOi0LTHRefqfYlqr1dlkc63hmw6UCfuIEni3aLFlHttlANNbQ05x2DIBlgoWSt425fSaC1CINHinYAMMCe4OzSNprZ+2fl+Qo5J2Qd/qmCTuy9l+yu75Ao0iIlHiePjI5MEe47LuvvfbajEC3EsJZsxfKAXYsNC6++GJk9GIFrU47cZN4GjtdYzeF5dh5NO2OxQstRfHKK+XG8saCLgRQyQUJ8vgHuae+5EFpfGpiOSqkoFZ/0vekMbIl5cZcrNB8hPimQpJUTD0cSkrd9616CkqpLzglew+9cBUrH286vV3MkIqsNw2EhogvCI47rxOjEgfCg0U2kGpLL61WSkuiVDlrDk+lkgR6NhxdGZ2EDx6WakaVVlCIzlKnbF+23HJLd0sXs06buVJhP63B6aJaCdDVT8yrqvGu8BobSjlpmMzpHJ7Sl5L6YhoszR6+Jr220Fl55ZVTr805rapN+TZedpaoTaNmW4DH9OJLb09G4912DMCuTffolAgu8pOJAegGg9ycOZAEo4i8qLHzLa04II4xpB2x9XXTNyyRbWY0xqx0UmFdNk6e9WLZWrqK3e9uT71Y6qcmShIJdCKDYWubmWsCBHCgI7nKe20TtPWvr3ECVbV6xGejIxYK4hNAD4Z6pC8WffJbPTWufNqLO++800STWbulGSWq+rsykWVizIzqtFXSBZOpXpDXjfmF58dLCQ9yo1FJgiXNF+kv8xjMrHGiLNXQ+BMUKMzhuXKdjFLIedwqTVipBg3hNHmR7m1nWp3Fp42tjJmD8YsNpWuuxPstniyhENDKBAMN7pauNvzVN96GjIRDV0YfYr53HcmTADlhentL0zS7GDgrmQSGvKNAKrMNnemWL07HxUPNH4jpa8ynel2gJQOwuk8KAJqxIhHJbgEDaGopWCzZh7Q1Qn77yRxLCzoEZOmEsWz6ntldZsutXBVD46afUO96ZBeZPqFEAx5gvTb55nwb5DlZhaVyL19PRTRaLLmhen8mMMc19l1H0uopD5NXsbFYF3PYeucWHTtFXtQoAWvfHNVfq/MmBb6f5BLKhAKB4tWUqaOBn3xW25AFeYSSIMHo7M9Wk3X7rlk2ZV6SqjItin/V/qlq3iWQofIp7s9IF5rO6WYY80x+A22jcfdxdcrEQiKUaiAL8lLRoo2rhu4Wbs4AUJl9/UsMIE2XBBe9UyV13kMLz5133jkpl2BKhFKaa6iL8Wq3TK/UxeldN0n5HqyGsIds2mX4KXtp2CgJVds5DV0piTnRPTBwsglILwd6yKDROTFWlPqIk+WFp97Z0rGjdasrNLeqxGaLzFRzSZlsDmU2iuvoS2l0WtVQzPeIB83FaZOUhokk3TAVlZzFR7qVNigpFmEeFJ7n3o2mK4lSo3aNRHzMxtNoFv+CRWgKB2yUHunFT4t0NBgCrxYY82KWQM/SFZi+C+vQU089tfPR8RHZU+bIbr4+Y+07quYJfe1RNUyYVpL6gkJHTBfUS6XRgRt8qLswOUMJTyXNPKJwt6+/6V3bGm8vHpOWF4sssoi5SP29/jCbEtOcARABezl01cvqG8hPYvskWfLHlKrnR3qdMDYmykStTRnzuFKLxQHeYostLPmJXHzS+avWHZ1iL2RCmdhqqNTihH+aWbDV9JKhyqeFyPEuMbSOZyRLrbyrIJmh4ehk5pow8cUHNQRMNirZaQMf0hcT37iWvVTl9JkeTDyemkQMyH4Ok29Sc7R/6Vv1FzGM2X2uxf6W0szG7boS/zOOpcupar0TKpYowQCS+jFRzg0ivwP6xX43KdLT6FjRtx8dX4cQ01TluWsStheqmgBrL5Ha/5/AsZxPkhxjxCBCR1wlSiCWYpHlXhtcL0DmpqXyHf7UEM1Zeje8VC7zkgVrh493q1hzBoDDJ8qo2ootsZJO+bSXxfzBps131EGJMHNEozkBwWi62/QvSWilulPaZpLdU28Qf3cIsgW4EBTep9xZ71n7D7vDmidQjAbPDJ4pkbDe7JAY/AM7z8/CoQ+GUk37aNWflZyJnu22267Ryt6zXkWx2GwUMtmlhHVfh91vSskEMpmuEWcnMiwISqJFC4W0eEoFCKaaij5Su7qWGGEqTNzfldO/J9CpST6Cr5O9UIzl0cECufs0Vms3gN/nhZTyVC/dEn5YZ2c1cqKE4WlTk6dGwrqV05wBmIMSQUSfuSVMPtnJ4YQWYjm/CgmThbVMepXZ2/IKLkpO8LO84Ur9soNT3uEbWG7/BT5jIuYj9B7YpCMyLX5tsW2/2ujEjA5TKKaQ3iF99JRniePJN4l9x2yxdwVIBpifI8aVpg+WNkhtXGolGiwz3eWBmJeu6dnBHvyNJK/KggsumKJOQdiuUTwG+en9od+TJnMrzqfpZct/GYNbL/cO6lY120h5f7zzKCFJo9tj1ZY+EH9FyoMw2tLo2KWZ4PLoWOrqF/A9lWpQCU2pE1u7NQ+2IrsX+ZbtukManD4rfdFxZn4+nJIEhnrcN0hMn4ZP362GrcO6a6CBB+DQ5OoZW2k+p+PVNk0Yq+YMINsLFhUUBIVeER8Ac+bi9Drhtrv4IHoMLc/VNFo+QvYJxfp9pQliA8+IUFhd5UtDXixfhTQKXemkJP1COaUFC9dG+QPDFW5rCqSlimHyiqfHq9MRLCqtdxCJVLvJK6+8sjQEFCHMJd11paFca6217MH1pSIdQSH3gsQGUkd0yqDwY0JzmkMT5aW/LC/NKaX+9q1TAGQbmn2ayD2IFtlQICB9O16qLJHQEcapJn3TE/9kP4tLYC4XFiiD6shkEDPVCiVZHCbaqabmsFYt1r6513xLbfh69xKq2YxUtJ4AspdqXPbBE0OmOQNIiy8+JtmuIKlMvdN0ShNrqQ9PmVAYICPSyNnbFlu0okkMwAqoXvJKrIsevjibYAnFWGDU9T7XYgEmT9XsowmF0CA7xdh386PJw2T4khd36otvQOE+fAOZgM4T3i5q1SLm7dM4d0mv2HlbXSwpIFXxeBYSNpuDXD9Vh5MGckcwibyiSpnYnvEquVzkxyuewLmz7VPqTlOHOBtQkcT676QJPRLObCCUKLRI6h3XSePVhAHYzCa1eFEo5vVNNFGVVHmkTXwsumzWSgbOXm5icSbkUK7a9mVMPLFhSxJ7cNvDZAzDVsEuLV3JLt7H6S6llpIWNWPWOcACFpV5saM7/+rHXMm+wGtGwiCTKnWARI7ZtEHh1pTX1OnraPqXTHIgkp+mXRCz3ntCspE2K+ZEOglCwqSXJnlgCFfax1hOeYR7/ISdIZpS0p9MUlO943WVI8ToCzEpO6iiCMucQFCspJczdd9fKDEwwSP7NmPYh1HgoTB9IJC3CkcV2npEQ5kB0IxbraT3uMgA8pmLqOnPyHW3FToAnSJe6G61/a9NLBHi19JEY8Ysemv3n6rxtnhr4XjnUl8oDAYr7h9XX5gG2n65EmMu9cVPmsY2mptxtdXFwtaVZGtFai2b0mKTes/OLN9ipEi6WEeBD7isAou7Z3M6JU0jjJii+IbZHEjfWeKxmc4WU42P9DrHMi5bA6LH4pVesxeNlhkAe5g09lb6xe1h8rx3q44MgDTNKgzxQ8AAvAQW+OxZ0zD5S9w/KPOYCb+RbRgAJ7gJVzvAB0WHzSOSE9QDlT04wfqX8DAbCJGMC8kAQB9+op/IlJ3oAMO9TH40s/5Mj1h8ENw1ikaFZhNwIg8ZNmCNVQWPNjRYdmdm3KOF0X8wAEsAUldYaJWbQx4ATr9JG27DWwUH4ExYh4lsBjocDECvLSpJSNJVQSumMcclMwAyK70QXZyMMcU6rikDMI/kSUTCgsNOuk2MsDEh6k8B6zkuXezN0My6j2wkL4SLkZn7Q0zXW0mzlr/YWGksjBeJls10Eg3ZHJCGUXBW6rAmYh8mc6hKLghFl6xuYfUfDMDknuRfICs2sNlmm6WXm/9XMb8u6cQAdIoPTl1oHm46MwMgIck95e7gNRsCBlAKTZo7WNmE7yJZkaXPPP0VTauyBHdIWGIAjpMslTerZrPL1Fmi/75555WI6eRnirrRCwbwmuKQN00n+tyiNOcn0rRMxTN1AYUYadNgvBUnPsirBQI2x45DSKRSOBWlCtWn33dx/PHH841K6z8Ec3zLXtzVp3+8FBKtMAtMT1G02oAKVZK3PuOtrQ/lLWHJ6HrR0L+1PcXazZX5J60Ak2Ho8OHMmoB8t/oJCxme3IZ5iF/o6o/C0FPI9SRZbnADtlKrXX/NLyxErYuFiiNUZAXPO6x2veiQYNIe/m4Kk3XTnzE/a6XD77DCLhbjaObi916chM29ZrAutpKr+o9KWZenG5Q/uYSdkYtlEgF6zqxLgvUCsRXjKgyM/1pdyA46a4cAO+N0jhDFb3ITq10X8DCaP/Kr2lE+XoK5g1GxuqwOLWqrM/vriNOebR9ZGI+3UxMr/28GQANsZ5SkJROrq4JPYQCJmbVx0K8g2UFSTRHw+WSD6Zp2IcgeKQT+zQA4ILD71Hlbj6whsfZPmTUFRfwcPADxPLlzp2ralyA7EAgERhOBZIzbi77/mwHwgWZ1pA2e0CmgmLT4sQcddJBE0aK2F3T0qE5iTZa/Ki9Gx+xRW1Ft5wiITZZc8zp/pBYlKVFr+qXUAt7RIZL7iKM7cn+FiCChyT+7mPgnA1A7BqBexsuOxMwNpGNhrJ37JpPKTXcxwYq2Rzr0LhI5UlUxyhZncJi6zOLQaX96VDpZfpj6GH3pGwI08P0xWvknA+B8z/+NppStZym8CQ803uGUwH3rfLcaMsUIJaY2pwWIxditaqOeySPAmZkL0uTrqU4NLDc4A9MBiJJfHaqCkpoiYEWeDVV72oV/MgDCH5fQ2PlkldQq3xxRoFm2ZQPhnlLTxcr5l/Jfu++++9TJgqp29HcRiqiqDwiY+pO2qUdb9T50IZqoDgIPPvhgtsnsKVUvMwAhk5KvrKMSHH9TbE9EpGKE2OKtiqd5NTuxE5FiKjm/qeLUBnl1R4AIiPKs7r0I+quMgAAwXV9evMwAxJIU+NPRfWp3ht8QCGctxxy7rGvW/kILOKyjyuMatA0BAo46OOqoo4agI9GFKiBgypp//vkzJSKmmMpE67MzyJldSfxTBORoiLTSv+OOO0hORArsSu2DqsRmnFeO1nn6feELXxgUGdHu6CDgi01HxNh69mfzPjrYjmBPRWkVnih33LEEzFisa3NOtxL/ZACqExDcAWkSJCc1Ffs0gkKhHdL/Rlgip+sIOJYnfT4iaw7N59N1lKLCiSGQjgab2LPtn/o3A/DiCoWaToNxSBChUD4avn0VFbzr8IRk1cqMtVJ+3hXEKkjqCgLsmr773e+qis95iq7clWqjktFEgFGZc+pz3/1My/90VlLOn3zi3wxAXVYxTsZxcoLj7pwr65yjOsqCxHr95Cc/mfy/Jg9Q1BAIdIKAo8HOPvvsTkpGmUBgTATEX3BEdi6WREB+HnHEEcw1c/7kE//BAFTHRfOSSy5JpyeLlLT88svXzl774YcfppHTF+5zouhNHqOoIRAYEwE+NHX0lWnVLyu//tihtyJgxPPJLYqmK0kJDJMjjzyyu2FBywxAG/YBooGnATCZOjtURJ26jIcXNx02QvovLqPPsi6UB521RsBX43SzWnehSLx4mTX66ouUD0d6scUWa2q8Llxrb3cACT47Dh5hidXcddddTIN6oYDuxVD98pe/JCZTsyivq622Wi+aiDoDgUYE+JxffPHFjfk1zWFzEtYTNR27cZHdZAfgecFzDjjgAAcApLMoHdZ67bXXjqveKBwIBAL1RUAIgHRkSn27EJR3gkBzBuBJxgwMQ5MRPWngxz72saJSopOq+1/GNuXMM8/Urr3Lpptu2n8CosWRRUD0ro985CND0/2InzjYoSxFA+0dMS0ZQGrys5/9bDqq+w9/+AO7oHSoeu+omWTNpGNnnHGGSjCAT3ziE5OsLR4PBDpHwCKJwqzz8lEyEGiDAAZw0003NRY45phjeq4ELrZqIcAQKJk3sEVde+21/S0WqFT6/vvvT7Ey7F75AFeKtiBmuBFgdjwEMVSGe4yGoHcLLrhgd3UzY+wAQMYtQKRoWwFp06t4FJdffnk1oRTSTrwktFFghP9XNccoqAoEAoExEZh99tmTT24qKSRzijU75oPjLTA2A1Djhz/84e23395B9dLHH388N6tLL720u9ZIbejWEBOLdPG1cWJyuko0WIIl719HMkX0/zZ4VuGWESRRrAIl3aJBNFAmet2qLeoZcQQYMabIIgmH888/3zRoUdt1wUanchIOYiZc54KJs//ss8869oiz1Tve8Y6ejpO2OKMJUr311lunhqh5XSmNBkaf+fiaE0888ZprrnELVYyye0pYVD5JBKaaaqo8cJOsqiKP+xyOPvroihATZAwlAiTw+bDebnWwUwagPbKg0047zbQrUAR2tMcee4i0vN5663WLFPWwpC6qPvhDtzI/5eK7wQYbFCf6xBsAtOaaa3aRpKiqRwisvvrqeEDxhWZFQ9s066yz9qjFnlY73XTTeSGt1HraSlQ+ygice+65zmvprnnuOBgA6PEAblZedAIZcdbsCWgFPvrRj05AMU1iYyehzttvvz0ZGkkLqIK7lMaYT4r9dcokjEpH1rz1rW8tukq/9NJL6UHf4VJLLVWqIX5WEAERyF2JMONu4JZ85aogqZ2Q5FAN5hLBADrBKspUB4HxMQB0E6+TzHALuPDCC825gkbQTohW2mGX7JSF6lTYyQYCG7V5ykn0WlGAhbUIGG1KWvuzjtp///3blIlbVUPg3nvvZbWVqPryl7/sOC2cXuiODTfcsLt2Dv3puGigljL9aStaCQS6hcC4GUBqeIcddlh00UWFizD5Svtic/igEmUOlHflTFakTaU6Bx10UFEaoDwGMKZ8CTtJNFx00UUesRHJm4ncYiSqhoDt2jbbbEO788ADD2TaHD4qzf1QTHLvEulivlWLhE1M8kGpBbVBZCCQEJggAyCCJ2mhld5zzz35iFHS+qqFjy7Jgu6++24euRZHJbjJeaecckqZDEznmGMOCfIcH3+pWONPew4sRIBSbblLAFUMWC2WdfEYncbHI6cKCFjjEx4mZb5XiMEDqv76178+9dRT3iKCTj7nOLpYflWgdrw0pO6M96koHwgMBIEJMgC0Mkiy9r/hhhss8Fnfb7HFFmRB/ha74WSMeeed9/vf/37KNMUz/rPTt98fr67vuuuue+ihh+yyL7jggmITOc2y0KakxIHy3UhUBAEvww9/+MM0+3uF7Px22mkntDl96IQTTjC4OPqf/vQnTMKaukajSSm1wgorOIn6nHPOqQjUQUYgMCYCE2cAqWq2QGJVi7zPLiiJccVgyDJc7gyC8zzyyCOpsA9ehM72XzUu4mD6RvUAkfGTTz6Z6sFCJHbddddiAN73vOc9xWOUU8n4WzUErr766p/+9KeoIucxy2dRz1xzzUWRs84661x//fX8+CwpDjnkkBoJ9OxdWEZgbHvvvbfT9KoGe9BTRMBkZaRMU+3nouIjw5qeLANgyGHdTZ6z++67k8+Q7RLlF33YfNiuMeEjzEkLQ64GXOoJBBofoSFcYIEF5FsnTjvttGzJI9x/I0p1yZl77rlZgpaoXW655awSCAYdrX7nnXeW7lb55/PPP0+nbU5JPvNVJjVos/jgM8Sa0SKD+ValAGEJTZjRN840WQYAO+txvsEOYhQqjnEnGS4NQYdTs/JHHXUUhiyWFmlAkgw0jocIGIzEp5566mQD2lggcmqBAOVNOq6n5OhYJJ6ZmU0ku6BiZo3SRd+UGpE9UqTaXz733HNcR007yy67rNgB1ek+ITl6zKL9IakLDCARattOdMNrly3Eo48+utlmmxVhNcWL02C6x3VtlnPfLPzNCMV5X/i5JEGyj85n7PEDmGeeefJTkagpAl4MEh7E0wCRHDbtBesgh5K65Z0hD0zivqYlK5XpFSUCZa9MHCpgSScWDZWif/iIMc8UJ5ZiB601uYs6cstkxR6B2sZ4sTuoCPNGjPmzSHAP0zDq1sW0Px0mjFxWOpiBmh9//HGasWOPPZZZp6tVTxZffHF+NC7KQLtpV5LTdYu2qGfgCLzwwgsEd+kFEOekFT3WBCmojqnfa9OqWAXzTz75ZL2zfOEtT5VdQQrHJMnXympDLw488MAxC1e8gDk0LSXFiCROLFFrbWH2Z6WSZ6RFFlnETMWmsVSyCj9Nj4lOqtDu0tO1HQD65ptvPmsfuNPySSCUEviWW265+eabM8qlBOUBQ1LjxJC015GFSk3Hzz4jYEKn9T3uuOPat6sYNYAyPlHMoH3hCt712ltLWr7QZzhKr4IUtiLJ7O/jZWuXCpx00knUbMneutUj1cwn3iFYdpatsUAhBuCtY42SLQ5kErKzULfWNkElzn3PPffYldJB6nh6AyvSO5tmstNeEdNdfqI2Z0k3hmIGKDs5l68CN84X0GOl3/UhqGyFPEJSENA2OwDEMyjwuntV+HxUti+NhFn1M3IVNjF9q/a7Fm782ir+huOy7K+QSi2fZ5l3vvOdZn8frHmzsaeVzbHLZDlGZZg7khMCT7FYEcWA3L9IP32A6cjLlqIdYwysWmw9mbQUi/U/TWCORRmXLAlnds86pruUvKq71anN+yQ6ULL8Sefk0d/y1yWSc7nb9RajwhohYDnmmyRnIOtrSjb7H8agypDSNi1Q8UxnOSVbtTT1WPqkc/SqSbZtFpPros0JgkXceu9735voP/TQQ6tJeSNV5MaEh0nsk4jHw1KEwfTTX73jK9q4sDAv2QrkYgDBD6gHxKNsbKgPOeKkia9T7AsBiViZXW+6+wwgkWgpZBfGXrPrFEeFtUYgMQBfmthNTTsi2mD6DmvKAHTqvvvu8/KnFaW+iHNHM2xn3LS/g8209jcn5olv1VVXdeCH2TAdBi4/MQBBO3RBZmV3M8LwFb1QxSkwBKT84PVGcVfKfZRYa621SvsAxcSW90jWYqbyA9GFsJchLC0SzDSmF7O/XveKAQz2tY7WK4sA/94UBFTwD4Fti2sxAiKTfgqlQPhgKV3ZXnRCmMUjebr1Zlpf6xdLNhMKMUUnj/euDKEHSQidJ3pIa1lsmx+R6jI6qV3irJRjKSrniiuuMB9Rz9Bq9I6wCdRMUPOhD31IR9iIo5DYbYYZZqB0NJsXayNO0R08mFYyTaxzzjmnpxivpw7mwk888YSSJBbJjstbqhhRjLBjBBi5WC8SHAnFO9Ac8Ula+/vLcxY9fGl7xHqDAfRiKKPOdgjst99+ppL0HTIcYISXLrN/yvSXFLFHb3w7ynpwj4xFX3K/JAipez2VtOoHSAXxFWQ3yxbMnvZkrcqnfByL1z3KrUOtkXNhu/wU0ynn9DNhCc9YHv0ZWwIf+tv2r41VBdEKDUd6Cg5ikfHga6R8n332KTq0Ksk3pX3ljZV0nmOP5VvI44I8mgx7GruBziuZQMlgABMALR6ZFAIkDHvttVf+bhsTzChTWIVJNVOZh/XFSUpZM2lDYHIZCHXcFIoemmussUZppdyUKsvnHXfc0TAVZ0CzP6WxDQS37aZP9TqT10XxzWG/9PWvf73DRsUjKZqk24myTWjcmbG9KTqrg04rotl32EqHxXwOPBJsyIrdsUFhINNhDZMpFgxgMujFsxNEwJxi4Unemo/68UH6SX362GOPDdwAY4K9avsYoYrlJxckPTWVMBbi79a7FWWmhWG72CpWl1pMsx5piaYR07nNO/2q8nk1as76yle+YsJiWpNFKEL4EeLldnuR0JZpkRehviQRDf0K/1O0jbdpQjBP2Qyl3YClNxmdagm7ioMCIsXY9Xo5E+9k2KKYbcQk31IyJXxIVdpNQkLfApW15lxF0WgvkMx1BgPIUERiAAgQboof5aKNHEDzg2jSsXpprUf72vmidWKU0oLaTuWlpYmGVEH43onVlp8yPSVhBQvFlGn+FcDDApmCIRfrboJ0vijwwcw4DxH3T7IV3g/ZzQpQ+EordesXv/jFoogG85iYjawdoSgURVMx7bI99S1Msi8TeDwYwARAi0cCgYkjYM6yD0hTiQWsCZpykqqguPCceO3/ejK50dFhFmd/Zjx5Ff+vghP5v+222+IlLpEf0/Om0dSQg30mUmPrZ8CiL4KACRKVmkhNE/e3fmh8d+BPQKfaNChAw5gbRwR0tgIW6RlSfqyMaJXssD19YVVVPOhCi1jOWWedJYxCh5V0t1gwgO7iGbUFAmMjQAlMHG8NaNIxmzB5wgkoWhvdfAhbRMnNxjmNVRNlNAqvyalV6Er1zzbbbAwfVdJYsrHCTnJEeVGbK6tqFlpooTQtFicyEpvOpUxN29X9U089VUeyrarVOmGapt1q+sjEMtGpTkoOmmQdodvQqJmafpj4q1inLQ7dST7OhGJcSXLLYpnGtO2Lqog9izoYm5iDDz5Yu93l/Y2tt8kJBtAGnLgVCPQQAWyAzCTNm+kvP6bSvOaQHJM4btFIB6mL5aQJ0eSS73JBkFkMarbYYosVJ+VcsouJq666imQ8dQFDSjXzJ8B1LJkn3BDPLM4iiYelyoVqIDqfcIWdPEi3kZlNatTmo3GNz2inqLYV/KZ95UVBnGqNmrDhRx99dPun+nA3GEAfQI4mAoHmCIhaI9hOXhWyjqUh4NOUS6eVNcPwxinGCZpphnJ0di6fA9Kp0+KU0efDDz+c7/YoQRZPlKFF5vPJwcrCVuvIKzIA+48Se2tKjzL0q7yRs08WWFQufAhBU9NHuphprndUAMN/LSZO4C8/CcS7ig0BlhY3XY1bNyVV5RHxj1dZZRXeCdBIHeFlPRBxf5H4nA4GkKGIRCAwAATMEQQp4uA63s7KnYlOUbWYRStmkNJUnhkAS5JMd2IAlt72B67+yBbYdOEBmkv+DVTESdGNZkfGJtrMhsxdKCEyqU0Tpn4KhiSHSezN3oIRrcobfXeb1tCVTIOiRVsQSo40cfPr1h2x69mGdtIETNg6eyrpFVRi1X/YYYeptim36KTOXpQJBtALVKPOQGAiCLCpL87+qsgMwAzCqbhYaVMG4DQFxipmmWLJPqcTE0Lw9ttvn5sWFYbImyK3TXQd/MPRUh7Ml21ECZBcYX8SXHNzLLZElWgZwlW1b/3yyy9PIc1zR+zSemcf1Z6Y9neDAbTHJ+4GAoNEoMgAnKZQlKhYY6bjJ4s7gEHS+q+2EwMgu+erJc/aH0NKHlUsXshM/lXw3/9NjnQGSy65ZJL4WzVbOwt+eccdd/RnE/NvUhpSlLdslNGTtREOEeP00NSeyuqej0Uxsr3djMA+2VuiofoBZwQDGPAARPOBQBsEigzAcpLLaF5IkkUkIbXphk6yTSV9viUCtmUyh4Mk8RfKhjw9zZ7WxfgBepwNR3WcLgF5yL5ydBCxGQQFMZN2ojDoT9eSNN/uJMtz9Mj+Jgu4EhlGgcgo65Cl4UAgVp2ONMIVDKARk8gJBKqCQIkB4AE77bRTWhRbVqeYkdQGZtKqUNxAByv7LAlhUZPu84TImcUExe9tt93WUEclMmy5Dj/8cMv/TLDVvR5xRaa6cIhFjvahgLPGuh40ohcoBAPoBapRZyDQHQQSAzC5UCG+5S1vMbNwU0rG9ZaljETlcEeiXexOez2oJTMAUWCTEac9QbKKSTOpZTVTev0S3M3OoAckdLNK0ZN0BLUproYuUBLks3RsdNzCJ6pj59O+88EA2uMTdwOBQSKQGACrRAajFABpxrTwRxMlcJJI0AEMXFDeBqPEAMyMSQ6OVG4NqSPpr6CbnK2SaKhNPdW5hVQX26Siv4W+COcgqIlbVR6OEoz/PguiOCSRDgQCgeogwBLGJiALoJn6sEckLfExJyLzrerQnCkR40zoGxTSo8o0P4oHnu86p5ftU15N5/wqJ5I+g5KDCIiZEF9lvtyHHHKIcyVF868y5Y20vTq/Q433IicQCAQGi4BIxaxQ2MKzhxG3kh09sQ9zSTITulOuA8izAxA/crB0dt66U9eFqMMMdEHoTZ3KXsSdV1KdkpTVrEIpfo1CdajqnJLYAXSOVZQMBAaDwN577y1AvLYFD2AtQ4RCnMLYfDDUTK5V0yUJCROaVnrgyVXf76dtArLHcr/b7kZ7L8eiiisQCAQqiADnqSeffBJhTkzLbl9s/5PAh+K3gjSPSRLincsolvKYJaNAHxCIHUAfQI4mAoGJICAeDgsT6l8m/2wQUxWkDcQmmR9MpN6BPiP0sR0Ap7aBUhGN/xOB2AHEqxAIVBQBRibJq0iQNe5RiUpTJ+l/6TiRinagGVkk/jH7NwNmMHnBAAaDe7QaCEwYgYUXXpjJfD5Nc8L1xIOBQIiA4h0IBKqLAA8jagBH75ZItAMQOYeBECu++eefv3Q3fgYCHSIQZqAdAhXFAoEBICAGMltJMeWbto0BMKsvnkzStFhkBgKtEAgG0AqZyA8EAoFAYMgRCB3AkA9wdC8QCAQCgVYIBANohUzkBwKVQ+Cb3/ym0xbDe79yA1NbgoIB1HbogvDRQ0CE4aOOOkr4gdHrevS4JwgEA+gJrFFpINAjBH772986gLdHlUe1o4ZAMIBRG/Hob+0R4Elb+z5EB6qBQDCAaoxDUBEIBAKBQN8RCAbQd8ijwUBgQgjwCHPO1IQejYcCgeYIBANojkvkBgJVQ8A5MI4fqRpVQU+tEQgGUOvhC+JHCAGHZ2222WYj1OHoau8RCAbQe4yjhUCgGwg4jvzxxx/vRk1RRyDwTwSCAcSrEAjUA4H/+q//qtfZufWAdbSpDAYw2uMfva8PApb/Dz74YH3oDUprgEAwgBoMUpAYCEBgmWWWWWuttQKKQKCLCAQD6CKYUVUg0EME+H8J/tzDBqLq0UMgGMDojXn0uJ4IXHnllaeeemo9aQ+qK4pAMICKDkyQFQiUEPjHP/7x0ksvlTLjZyAwGQSCAUwGvXg2EAgEAoEaIxAMoMaDF6SPFAIzzDDDbLPNNlJdjs72GoFgAL1GOOoPBLqDwJvf/Oapp566O3VFLYHAKwgEA4gXYQgREDZn/vnnv+yyy1pFTt58882dtF6vk1V++tOf3nfffUM4WtGlwSEQDGBw2EfLvUHAiYkf/vCHf/SjH62//vp4QNNGnnjiia9//etbbLGF81WaFojMQGAUEAgGMAqjPHJ9TKfmspp3eNbFF1/cqv8XXnjhj3/841Z3Iz8QGHoEggEM/RCPdAd/97vfbbXVVjfeeONIoxCdDwRaIBAMoAUwkT0sCPzxj3+8/PLLX3zxxWHpUPQjEOgaAsEAugZlVFRZBI499tjPf/7zlSWvQ8Le9773rbLKKgp/73vfu/baa5OYq8Nno1gg0BSBYABNYYnMYUDg1a9+9eyzz64n5sqrrrrq17/+da17NfPMM7/3ve/VBWdDrrvuusycnnrqqVr3KIgfOALBAAY+BEFArxB4zWteI37Ocsstp4GHHnroK1/5Sq9a6nu9f/7zn/VozTXXjADRfcd+qBoMBjBUw9ndzgg+c/PNNzuJcKONNpLubuX9qW2mmWY6/fTTF198cc2x+1xjjTX+9re/9afprrfCpyG5NWBsr3/96+1v7rnnHkOz6aabRpTQrqM9IhW+bkT6Gd0cLwI///nPV1999UcffdSMaa7hM7XnnnsuvPDCzqUab1WDLU8K9Pa3vx0N+vKLX/yivnOl3cwpp5yiI+uss87Xvva1DTfc8Fvf+tYDDzzA4+H5558/4YQTUjcHi3a0Xi8EYgdQr/HqE7WPPPIIR1nihbReJkP/xje+sdRSSx144IF9oqCrzZgrp5xySlWa/c8888yu1t2nyo4//vhzzz1XNNA3velNhkZYiPPPPx8n0LxtAV+HT33qU8ccc0yfqIlmhgYB33ZcgQAEzCN/+ctfjj766A984APzzDOPN9zCf6qppvrsZz9r9k8TqOW/uy6etJUFTUemm2469L/2ta/9/e9/n7p21llnTTHFFDJNneh/61vfmj7hW2+9tbIdSZRfd911CH7jG9+YCL7ooosywdTa6L/00kvTtswYfelLXzKIEMhlIhEItEHgZQOJuAIBCFx//fVmGZNmmmjmmmsupiZ2AKT/JpTvfOc7K620Uj6UnEUKyUM1cWtkAOiUud9++6WuFf9WmQH85Cc/yZN7pvmcc84pwa5r3/zmN2eccUZlDB824GepTPwMBJoiEAygKSwjl3n11VdPP/30eZaZd955xVNrROHQQw/NZRZccEFKyMYyA89pygBQxXw+mVHmLkhUlgGQwjH8L5Ka0vZk5513XiPIJv28rZlmmmn22GOPH/7wh43FIicQKCIQDKCIxmilOcceccQR//3KlWZ/tiXTTjvtJZdc8thjjzXFwiP33nvvxhtv/La3vc18NOuss3p6r732evbZZ4nXmz7S60wGkVo/44wzyEYSDa0YAEoefvjhLEtJ82lmAJ7605/+1Gtq29ePBrErDjvsMKi++93vRqE53YhA+KabbiLXSoI44CtAQ1OqTY7RwZXTsw4PUOyAAw6Aj5pLheNnIACBYAAj+hoIj0MkQsqf5kF/V1hhBTreTuZxs8mvfvUrAqJZZpnFgyoheRBYrf9QPv7448svvzyzSDT4+8wzz6ABeSUdQCaMEnW11VbLXZbIDAAjWWKJJWhTr7jiikFNl9/+9rcpKvKgcPXCiY1IokeCFRAdRqJfH1ELgdy7nOAgZupPxdLoYJCxIcj4RCIjEAwgQzFCCeaDjpdKE0T6y6XoN7/5zXgh+PjHP54rcVbJdttt10+hkMXyBz/4wUyA2M5sVXWhDQNwF+tiUTPnnHN6kJ3rL3/5y9RrDCBpif0VOmK8UEyyPHE/9IpSuPnmm68pmOQ/epp7DQE4NLbOPJTNbi4mgSU0bhoaH4yckUIgGMAIDbewaD/72c/MLGTEZgSmI3vvvbdjRlzJWqYpFvvuu+8c/7ruuuuuYhmTqWePPPLId73rXUl7jK8oSwDNMr1YsotpSunTTjtNK+l8RH0hK0eGGTy3YuEsR2dbbWiskRUoTp1K8hTTEQyAmEj9JlBmNrnOXiT+/ve/i+uw9tprv/Od7zQiVutrrbUWwlxPP/10qxb1VAG9TuMIB9SeeuqpmZmlB+2HFDvkkEN0yvZI/RTFNkBapNtvVXnkjxQCwQBGZbgt8E0uZoF0ve51rzvooIM66fz222//r4deRfp8yy23ND5l0b3TTjstueSSueSOO+4oBmdjyUnm3HbbbfhNboVSV84k6yw9/oUvfCFNl1qxau6dsRNO9sUvfjH3RYJUjZCqRE+bnyW1NkewpmgYHQ7DxYZ233136pw2NcetEUEgGMCQD7SP33XwwQevuuqqaQqwzLTk5EZkAuqk83feeedxxx2X5OxqoGCkkEzVlh5nuEKMrn6XkriFVpqWLD3YyU/1FOc7TZjvbr/99k6ebVNGtaW7ZkYuV84US71YdNFFt9lmm8Zipac6/5kAYd/JPQ0bBtTLeL361by6HE/WeT2pJAS4bng8DS6OSC733HPPlQi2w8MDXmnn5T+2a/hcY1uJtuLfxjKRM0wIBAMYptEs94W71nve8x4b/+QoxJ5kgQUWoDYcr7kL8QjxgotHkomGaYo6SaLJlEpNmmgU45LKYVVJf5UUjm0CU1uqGZfCV1zi+aQj0U2afBRIgboin/nYxz5GXM5OptQREDksjPBELzA/xdDwwgsvlIqN96eGPvKRj8CkqMvlYwG0oghrXNWCyOOf/vSnk/EPgukStt1229IocxBTLF+lERH544ILLkBY8bJlLErJxkVVFK4FAsEAajFMEyGSvJ45v+kgXaTzjEwmUlHhGUtL7mD/qvJVm2yySatp66tf/aoVbi658sorm2IKNXWaPOmkk/LyVm1MlegtOn24g3KJpZGMn3jiiY1bIryBFCj3okOhWdNmMVF9oXbOtVmGm6Z5YDQtP4FMwY5y5RI49Nlnn91JPXfccYeFQvHZnBY+r8QqOqkwytQFgWAAdRmpTum0fzeR0X/m2d8EagdgmdlpFW3LUSHiAUnla5owszOqaZw61fGHP/xhs802UzKJ1IUSInpWsiSdaNWakkWrR/XYSXQ9BMWyyy6bJjsbC+b2jeThAbSsqQtgNCHio4q1IrsxP40IXTo3C22pSl9c2Mm46mmsuZTDCIreBUoqT52yz4ChVlopw+UzDbKjSuXTX48XmS6mNS7NRImq+FllBIIBVHl0xk0b2xsxO4lKkoSBcQgRNq1po1B43FUXHjDRkIfwAkvzBbMZ4v5sUF8o+P9YuWj6+9//vrDMCptk0UZ64/FisVKaiQ43BXUmyZUHiWJMxMnKs1R4kj8tb5lFpY4k8jg0lFgUWYpoSJBMfljJRqipAX5TYoj79TrN/vZhYvvAxNWjWRVKsEItAPULhlpfZJFFREJtJI88KgnrlPTOeMqWCCbeGVufxAb8tZlrfDZyhgCBYABDMIj/7IJp63Of+1yay9Lfyct82qBDxESjkJvjl8TplKtt00fojdPhXKm8WaZpsZRpo5CrNdtSV5JRtCk/yVtsRvM+QLtmeUrgpnUWg6GaH++7776mxXImzrf//vvnAEoqdzJlvtvrBI+BotszzFmFlhrNjhSY38knn1y8i3ln26G55567p0NQbDfS/UQgGEA/0e5VW8y62bDzibXM9CWbcch/brjhhkbdZncpcCiVVszXWkyrRSyBc4CNSKNwA8OwJk0LYWpkU48cs0wmifmNteeHPvSh5GBMEKFaa//SejyX72LCch6ztATWIhENbTnpuV6UtL5+kuTkzpoWKYcVcxWJtLSXY5djB5Y4mUGx0Bauh5i+i2SPWRUegNpMMNcBquD8FDrJi1BoULgUNDoHEBuiPHVBXzrf9OQmIlFxBIIBVHyAxiCPuxBpCcuNNLGKFMYCx+xTnFjHqGLSt80jxA4sRlKoNStoMw7D08ZwcggjkrbcziJ1UxL600VhkGQsZpzFFlsseZM1zkqTprdlBchzJQdaeOoFLlWSnGBsyhCJ5IjZL8+vb3iDqAx6wTCJJf6uu+4qJ8mv8MWll16aa7GnikyiJRFdvaFF7Rodoh7rA+e7FWkQOzoNBOWHYo0t6+wuu+ySGIC/4UjcCFHdc4IB1HgEmeg5rzF/n2YlpiwD7A+PAfGFstiBMWJTzTOGwbI+k11KCNJAbNIVE8+JQZGcHjJVtNyYWXHeTNUKrUOwnoulBAeLLFJPOQLnNZ1bJ0Zbd5/K8bElWtXsNIjcx2AArVCqb34wgLqOHWvIJIK36qflczBsm3AO/ewkQ5SiztMa3zalpPNkPLrDDjsoVpwuLf9FRGilRehbFyx7yaZsX8zvsDX9EYPsvPPOulDyeyASIdXRC9db3vKWPFEmvatMhpie6hvl422oEwZgA5RjaAcDGC/C1S8fDKD6Y9ScwhSIjSkO/ao1dSs7v+YP9zLXYhk9VvFJvpwmUHMoFTFbGt5VqXEEK3bttdemeZNbL0ckOb0kbXx14wR8j5N3FUmOLlB04w2lWtDsEs0tdQTPcDxvyqzOoJRoTj87YQBKLrTQQqlrwQCawljrzJdDRMVVXwR4MJlnzU1JmFuFjqS50gaFWJzVPJLMpC45flJLJiIRjOxksW7JTHhFoSqnCl1INKCN78Lpp59OVp56QanOMAZXKBKJZlfqiL4Lvsa9K2VWZ1CKBEc6EMgIBAPIUNQvwVpmn332KdJtnmW0XswZYNp62exJpCPCPjV1UoryGqMqKFFlvSxCdSmzIj+prBnXcuLVBVoNK30h25ZZZhlubk0pXG+99Zrm1zTz8MMPZ85UU+KD7DERCAYwJkTVLcAqkV9ukT4ClryvL+YPKs24kK2khT81gMiX1sWm0eSkViSJ1IjzVzGnUmmr+0996lO6YCpkTc/OVaR+AqtKETkZYii0BYttWgPDXK9Z01uROQQIVGjHPQRo9rkLTGUssQXgLLbL8EaInmIEm+LdQaXJRph1Mup3ylUyoBwUJRNuVxfodc8880xKF4J+AqsJV1W1Bx0CSl3BBToZE2fyeBRfddVV6ecGG2yQDi3IdyMxBAgEAxiCQfx3F8R6FL+ejMWJ7VUTQKMHZ/o3rfVM0QkntXA9yf8Pqj/zmc9Y+4tUQY1JTCcetcjSuQTrVSawtjtyRH8S3a9x65YLR6KmCIQIqKYD14RsgpTku+9wQQaLTUpEViBQQIDpKkHW+uuvLw8PYNLK3Tdf1hBp9qe84eUXs38BueFJBgOo91gmE7TUBzKKSikA6o3saFBPK8NoNfGApj32Un35y19m19T0bmTWHYFgAPUeQas2/v2pD77VbHpf714F9X1EQOAjES8Y+5eOBOD/JVMQC7O/V6uPFEVT/UMgdAD9w7oXLaUYL72oOeocHQRM8S5RUQUEzb12xNjMM8+cf0ZiKBEIBlDvYSX3d1VN31tvTEeVesrtQw89dFR7P6L9DhFQvQeeGehll11W7z4E9YFAIDAgBIIBDAj4LjXLh5b3b5cqi2oCgUBgtBAIBlDv8ea5w5Cj3n0YIuoZZbHBHaIORVeGHIFgAPUe4BVXXHHJJZcs9cGRJiWXzlKB+Nk7BDji9a7yqDkQ6C4CwQC6i2e/axOSTGz9UqtiRKfglKX8+BkIBAKBQBGBYABFNOqXFvNHtLUS3Y5kcSRkKTN+BgKBQCBQQiAYQAmQYfj5/ve/f4opphiGnkQfAoFAoJcIBAPoJboDqts5i+G6ORDswe5E+IE0HY0GAhNAIBjABECr+iPXXHNNxHAf1CClQ9AG1Xq0GwiMC4FgAOOCKwr3BAE2S6usskpPqu57pZU92qzvSESDNUAgGEANBqlDEhmhO3i9w8KVKibg8I477lgpkiZGjCE47LDDJvZsPBUI9B+BYAD9x7wLLTpa3ZHrpYrMPtdff71MB2+FGWgJnPgZCAQCjQgEA2jEpAY5f/vb3xh6vvWtb+UI1kjuMccc46yPxvzI6R0CzlNMlbeJrd+71qPmQGBiCAQDmBhulXhquummW2+99TIphxxyyJ/+9CeRQWtkAuQo2kx/rRPHHXcc+iG/ww471LojQfxIIRAMYHiG++67737xxRe32WabVVddtRa9so9JR6vzZasR06oFtkFkINAJAsEAOkGpTmUoVOsSCIjS4q9//StwLZ8jpF2dXrKgdVgQCAZQv5F0VHeYmlRz2Dhgx1ammkMTVDVFIBhAU1gqnUn9+5Of/KTSJI4qcaeccso73vGOUe199Lt+CAQDqN+YBcWVRYDwLXYAlR2dIKwRgWAAjZhETiAQCAQCI4FAMIAaD/MyyyxTY+qD9EAgEBg0AsEABj0Ck2j/S1/60iSejkcDgUBg1BEIBjDqb0D0PxAIBEYWgThPfGSHPjo+bgR4rgnBxH1h7bXXfvOb3zzu5+OBQKBiCAQDqNiATIKcT3/604LRzzvvvJOooyePPvHEE/vuu2+qeq1Xrp4008tK77rrLt5qGECK+bP00ksHA+gl3lF3nxAIBtAnoPvQzPLLL9+HVsbVxHPPPbfVVlt997vf/c1vfsM+8t3vfveiiy46rhoGXvi3v/3tz3/+c9E1fve73yHGvM8Lb9ZZZx04YUFAIDB5BIIBTB7DqKEJAuQkp5122je/+c0rrrgi3WYjf9999zmusknpqmY9/vjjW2655Y033pgIfOMb3yjS6uabb16i97rrrksxLUr58TMQqDgCwQAqPkDtyDM3rbnmmltvvXV1nI/+7//+78knn9xll10wAGdjvfTSS+b9M8444w1veIMwpVNOOWXuz/nnn3/BBRcoIOdzn/uc7cs+++xTkY4g/swzz/zGN77xzDPP3HHHHSgk83HYL/pXW2213IWUuPbaaw3EH/7wh1J+/AwEaoCAdz2ueiHw9NNPzzLLLOndEvrtyCOPfOihh6rQBXIeDMkpBfm9n3766a+66ipcoUTeX/7yl09+8pO5mAQ+sdtuu1lHl0r2/+c//vGPk046KfOqd77znZiTONuNlBAN6d1UU02VOiI699VXX91YLHICgcoi8KrKUhaEtUHgBz/4wXzzzZcn0Le//e0Ogm9Tvg+3iMhL56GLimN+bGz63HPPtWvJxBcTVtlOCGh8pJ85J554os1Kpuqoo45q2voDDzyw2GKL5WJTTz31pZde2rRkZAYClUXg1SjLL3EkaoTAj370o3vvvdc6mhTFpOkYSFH1za2YQT974f0h5T/++OMt3m+//XZN25Sw9KH7pS993/veVyTm/vvvt8x3bgHlsHwFvv71r1v7C2/30Y9+lI2NoNDLLbccQdCxxx6rO8Vne53W+jrrrGP5f+edd1rvAxZJGp1zzjnzfstPUAPcXeAbAjlO3yTC+p//+R9iol4TGfUHAl1GoLKsKQgbEwGiFdPuXnvtJQpxei1Mmo8++uiYD3axwGWXXZZbR4N9Cbn5Cy+80NjEPffcY5mcX1+GNPYxuRhLoeI8i41ZYue7vU4wVCW9wYoSedNMM41Tlxsb1a/999+fKjjrKkiKDjzwQCyhsXDkBALVRyBEQNUfo7Ep3GOPPfLEuuSSSzK6f/7558d+bHIlTOjUttkcngRcu021EZQWSrIBzUSa62+++eZS+060n2mmmXKZueaay1P0CqViXf/J1Me2I7drTj/77LObtnLQQQflYhIUHkcffXTTkpEZCNQCgWAAtRimMYgkvmCsQgqRjCytT5nbE78wT2xUwI5RVwe3cZePfOQjeUK3A6D4vemmm0qPEqf88Y9/dEbu/PPPn+ZN0hKn1Z988smEJ6XC6adVv44wqsl8ZcEFFyQ1Uk8vdAMogdI888yDPKBR9mrdvqQEGgaGrSqZNMP+6ruSfWBOTVGKzECgWwgEA+gWkoOvx4T7s5/9rOgJbGrurnIYp2Hazysqy0BWXHHFr3zlK84ibuw/iT+hSi65xBJL7LzzzkqWptfGB3Xk97///eKLL57YBpWser761a/aczQWnnAOrfUGG2yQmiDoX3fddfWusTa8KrM66g1c9sorrwyZTyNQkVNHBIIB1HHU2tFMxXr44Yezu09TG0NMVvk//vGP2z3T8b2irEn9G2200Z///OemT/MC22STTRINM8888xFHHEHO3rRkq0zMzFPoT5X4Syj0ve99r1X58eY/9dRTK620kmrnmGOOVtU++OCDiyyySCbggAMOGG8rUT4QqDICwQCqPDoTp+2RRx6xdCWoSQtwGlc7g0MPPbSperZ9M0xiVl55ZY+7bCkIQMzLKnfxfsrPWhQ7rHibbbZJJclwmPSYvk8//fTHHnssFxtvAhvQELNR4iMT8Ywzzqj+nXbaqStKDjIclZc4kw0Ku6bUC7ZVGp122mnXX399JSeA3nj7G+UDgX4iEAygn2j3uy2yFBaZeQErQU8rs3M6uPWa/XMNOMoJJ5zQ9HGWoFnao7w0gc+Y0p6mVTVmqkdt73//+zMlWIJpurHk5HOY8ydmoy1eYBtuuCEQutWRyZMXNQQCXUQgGEAXwaxiVWKZOak86TnNaKa2zTbb7PLLL++EVmt/Iv485xLpNHWIpf6lts1CJ+WJVoiAWPd30krnZVi4LrvsspkeGm/eBq2kN62qVYmn7FQal/PW+G4xZ0pNMPcMz95WMEb+cCAQDGA4xnGMXhDOEHTst99+s802m7U5ox1qT4oBDlltniTiF+eApaNnXSVxP2ZguiRcYjVvxqSnnX322c866ywlqXDbVDuZW88++6z6KYS1RcSkXfM1Gvbcc89UrbhDfqarqQKc9Y6ngMDtK1PC3dcjSd9A50w5rBUL/1wgEoHAUCIQDGAoh7VlpxjhMHdJK1x/iXR+/etftyptimco2fTuOeec84lPfCLXI7H77rs3Ldm7TOcfZAJIhFJDXIhz5vbbb9+4C0kMQJkPfOADmTYOB/mpjTfeuBcmp7mtSAQC1UEgGEB1xqJPlNxyyy0Wua405X34wx/GFTqf8pQk7iceSY+rh5LAMSlNbSh72iVe0CJJ8OFCA5FOaqvIAMi72HoWaaCpxvNQbr8iWGm+xdrHlmihhRZSYe+2L7m5SAQCFUEgGEBFBqJ/ZJjuWb/84he/WHjhhcVrIwwRd8FFMTCmqlPABiWTP5RplH0n19+SaKh/PXmlJdsU3ck0FBkACnlvCU2RScK9RPqUr9c4X86XIA0rGjUVb0U6EBhWBIIBDOvIdtQvSk6GlSZEl2mdnahQaK2eJFJnEJkK+8u/l89Bq8KDyi8xAHQWZVMUv/lEmqIIaFDURruBwGAR+KccIH/VkRgpBFZZZRVuruQhLpOjqJYk+z/96U+JSuwGEhRWzX4K1MPUh3gkFXZeinBpRSepquH2spDrFTGXY9yz3J/emDNX1UgNegKBQSEQDGBQyFelXet6gnIXg3eCER5kRENW+px+hXd2bbrppn6uvvrqpEbsO1Phj3/84wsssEBV+tBABxXFIYccwl/MjC/WG7ITD+ADcfDBBzcUj4xAYFQRGOwGJFqvDgIMZsQ6Ljpblb4J6tPqK0iTCIi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- "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -990,7 +959,7 @@ "text": [ "====================================================================================================\n", "\n", - "提取到的所有 id-smiles 分子对信息：\n", + "已提取到的所有 id-smiles 分子对信息：\n", "[\n", " {\n", " \"index\": 0,\n", @@ -998,13 +967,13 @@ " \"(1)\"\n", " ],\n", " \"SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O\",\n", - " \"E-SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O0:R[3]5:R[2]9:R[4]12:R[1]\",\n", + " \"E-SMILES\": \"*C(NC(CN(C(C(O)=O)*)C(=O)*)*)=O0:R[3]10:R[1]13:R[4]14:R[2]\",\n", " \"is_markush\": true\n", " },\n", " {\n", " \"index\": 1,\n", " \"molecular_info\": [\n", - " \"(Ⅰ)\"\n", + " \"(I)\"\n", " ],\n", " \"SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O\",\n", " \"E-SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O0:R[3]5:R[2]9:R[4]12:R[1]\",\n", @@ -1025,7 +994,7 @@ " \"(b)\"\n", " ],\n", " \"SMILES\": \"*CC(*)*\",\n", - " \"E-SMILES\": \"*CC(*)*0:R[5]3:Y4:SY\",\n", + " \"E-SMILES\": \"*CC(*)*0:R[5]3:4:SY\",\n", " \"is_markush\": true\n", " },\n", " {\n", @@ -1033,16 +1002,16 @@ " \"molecular_info\": [\n", " \"(c)\"\n", " ],\n", - " \"SMILES\": \"c1c*c2ccccc2c1\",\n", - " \"E-SMILES\": \"C1C=*C2C=CC=CC=2C=10:R2:Z\",\n", + " \"SMILES\": \"*1cccc2ccccc12\",\n", + " \"E-SMILES\": \"C1C=CC2*=CC=CC=2C=11:R4:Z\",\n", " \"is_markush\": true\n", " },\n", " {\n", " \"index\": 5,\n", " \"molecular_info\": [\n", - " \"(11)\"\n", + " \"(II)\"\n", " ],\n", - " \"SMILES\": \"*C(N)C(=O)O*\",\n", + " \"SMILES\": \"*OC(=O)C(*)N\",\n", " \"E-SMILES\": \"*C(N)C(=O)O*0:R[1]6:SG[1]\",\n", " \"is_markush\": true\n", " },\n", @@ -1052,7 +1021,7 @@ " \"(III)\"\n", " ],\n", " \"SMILES\": \"*C(=O)NC(*)C=O\",\n", - " \"E-SMILES\": \"*C(=O)NC(*)C=O0:R[3]5:R[2]\",\n", + " \"E-SMILES\": \"*C(NC(C([H])=O)*)=O0:R[3]6:R[2]\",\n", " \"is_markush\": true\n", " },\n", " {\n", @@ -1060,14 +1029,14 @@ " \"molecular_info\": [\n", " \"(IV)\"\n", " ],\n", - " \"SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O*\",\n", - " \"E-SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O*0:R[3]5:R[2]9:R[1]13:SG[1]\",\n", + " \"SMILES\": \"*OC(=O)C(*)NCC(*)NC(*)=O\",\n", + " \"E-SMILES\": \"*C(NC(CNC(C(O*)=O)*)*)=O0:R[3]9:SG[1]11:R[1]12:R[2]\",\n", " \"is_markush\": true\n", " },\n", " {\n", " \"index\": 8,\n", " \"molecular_info\": [\n", - " \"(Ⅰ)\"\n", + " \"(I)\"\n", " ],\n", " \"SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O\",\n", " \"E-SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O0:R[3]5:R[2]9:R[4]12:R[1]\",\n", @@ -1088,7 +1057,7 @@ " \"(b)\"\n", " ],\n", " \"SMILES\": \"*CC(*)*\",\n", - " \"E-SMILES\": \"*CC(*)*0:R53:4:SY\",\n", + " \"E-SMILES\": \"*CC(*)*0:R[5]3:4:SY\",\n", " \"is_markush\": true\n", " },\n", " {\n", @@ -1096,8 +1065,8 @@ " \"molecular_info\": [\n", " \"(c)\"\n", " ],\n", - " \"SMILES\": \"c1c*c2ccccc2c1\",\n", - " \"E-SMILES\": \"C1C=*C2C=CC=CC=2C=10:R2:Z\",\n", + " \"SMILES\": \"*1cccc2ccccc12\",\n", + " \"E-SMILES\": \"C1C=CC2*=CC=CC=2C=11:R4:Z\",\n", " \"is_markush\": true\n", " },\n", " {\n", @@ -1105,17 +1074,17 @@ " \"molecular_info\": [\n", " \"(II)\"\n", " ],\n", - " \"SMILES\": \"*C(N)C(=O)O*\",\n", + " \"SMILES\": \"*OC(=O)C(*)N\",\n", " \"E-SMILES\": \"*C(N)C(=O)O*0:R[1]6:SG[1]\",\n", " \"is_markush\": true\n", " },\n", " {\n", " \"index\": 13,\n", " \"molecular_info\": [\n", - " \"(II)\"\n", + " \"(III)\"\n", " ],\n", " \"SMILES\": \"*C(=O)NC(*)C=O\",\n", - " \"E-SMILES\": \"*C(=O)NC(*)C=O0:R[3]5:R[2]\",\n", + " \"E-SMILES\": \"*C(NC(C([H])=O)*)=O0:R[3]6:R[2]\",\n", " \"is_markush\": true\n", " },\n", " {\n", @@ -1123,7 +1092,7 @@ " \"molecular_info\": [\n", " \"(IV)\"\n", " ],\n", - " \"SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O*\",\n", + " \"SMILES\": \"*OC(=O)C(*)NCC(*)NC(*)=O\",\n", " \"E-SMILES\": \"*C(NC(CNC(C(O*)=O)*)*)=O0:R[3]9:SG[1]11:R[1]12:R[2]\",\n", " \"is_markush\": true\n", " },\n", @@ -1148,10 +1117,10 @@ " {\n", " \"index\": 17,\n", " \"molecular_info\": [\n", - " \"$ Z = CH, N $\"\n", + " \"Z = CH, N\"\n", " ],\n", - " \"SMILES\": \"c1c*c2ccccc2c1\",\n", - " \"E-SMILES\": \"C1C=*C2C=CC=CC=2C=10:R2:Z\",\n", + " \"SMILES\": \"*1cccc2ccccc12\",\n", + " \"E-SMILES\": \"C1C=CC2*=CC=CC=2C=11:R4:Z\",\n", " \"is_markush\": true\n", " },\n", " {\n", @@ -1159,17 +1128,17 @@ " \"molecular_info\": [\n", " \"(II)\"\n", " ],\n", - " \"SMILES\": \"*C(N)C(=O)O*\",\n", - " \"E-SMILES\": \"*C(C(O*)=O)N0:R[1]4:SG[1]\",\n", + " \"SMILES\": \"*OC(=O)C(*)N\",\n", + " \"E-SMILES\": \"*C(N)C(=O)O*0:R[1]6:SG[1]\",\n", " \"is_markush\": true\n", " },\n", " {\n", " \"index\": 19,\n", " \"molecular_info\": [\n", - " \"(II)\"\n", + " \"(III)\"\n", " ],\n", " \"SMILES\": \"*C(=O)NC(*)C=O\",\n", - " \"E-SMILES\": \"*C(=O)NC(*)C=O0:R[3]5:R[2]\",\n", + " \"E-SMILES\": \"*C(NC(C([H])=O)*)=O0:R[3]6:R[2]\",\n", " \"is_markush\": true\n", " },\n", " {\n", @@ -1177,14 +1146,14 @@ " \"molecular_info\": [\n", " \"(IV)\"\n", " ],\n", - " \"SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O*\",\n", - " \"E-SMILES\": \"*C(NC(CNC(C(O*)=O)*)*)=O0:R[3]9:SG[2]11:R[1]12:R[2]\",\n", + " \"SMILES\": \"*OC(=O)C(*)NCC(*)NC(*)=O\",\n", + " \"E-SMILES\": \"*C(NC(CNC(C(O*)=O)*)*)=O0:R[3]9:SG[1]11:R[1]12:R[2]\",\n", " \"is_markush\": true\n", " },\n", " {\n", " \"index\": 21,\n", " \"molecular_info\": [\n", - " \"(Ⅰ)\"\n", + " \"(I)\"\n", " ],\n", " \"SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O\",\n", " \"E-SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O0:R[3]5:R[2]9:R[4]12:R[1]\",\n", @@ -1196,13 +1165,13 @@ " \"(IV)\"\n", " ],\n", " \"SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O\",\n", - " \"E-SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O0:R[3]5:R[2]9:R[1]\",\n", + " \"E-SMILES\": \"*C(NC(CNC(C(O)=O)*)*)=O0:R[3]10:R[1]11:R[2]\",\n", " \"is_markush\": true\n", " },\n", " {\n", " \"index\": 23,\n", " \"molecular_info\": [\n", - " \"(Ⅰ)\"\n", + " \"(I)\"\n", " ],\n", " \"SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O\",\n", " \"E-SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O0:R[3]5:R[2]9:R[4]12:R[1]\",\n", @@ -1210,40 +1179,33 @@ " },\n", " {\n", " \"index\": 24,\n", - " \"molecular_info\": [],\n", - " \"SMILES\": \"C(c1ccccc1)C(N)C(=O)O*\",\n", - " \"E-SMILES\": \"*CC(N)C(=O)O*0:Ph7:P\",\n", - " \"is_markush\": true\n", - " },\n", - " {\n", - " \"index\": 25,\n", " \"molecular_info\": [\n", " \"3a\"\n", " ],\n", " \"SMILES\": \"*C(=O)NC(C=O)Cc1ccccc1\",\n", - " \"E-SMILES\": \"*C(NC(C([H])=O)C*)=O0:R37:Ph\",\n", + " \"E-SMILES\": \"*C(NC(C([H])=O)C*)=O0:R[3]7:Ph\",\n", " \"is_markush\": true\n", " },\n", " {\n", - " \"index\": 26,\n", + " \"index\": 25,\n", " \"molecular_info\": [\n", " \"4a\"\n", " ],\n", - " \"SMILES\": \"**C(=O)C(Cc1ccccc1)NCC(Cc1ccccc1)NC(*)=O\",\n", - " \"E-SMILES\": \"**C(C(NCC(NC(=O)*)C*)C*)=O0:P1:Q10:R[3]12:Ph14:Ph\",\n", + " \"SMILES\": \"*C(=O)NC(CNC(Cc1ccccc1)C(=O)OP)Cc1ccccc1\",\n", + " \"E-SMILES\": \"*CC(NC(=O)*)CNC(C(O*)=O)C*0:Ph6:R[3]12:P15:Ph\",\n", " \"is_markush\": true\n", " },\n", " {\n", - " \"index\": 27,\n", + " \"index\": 26,\n", " \"molecular_info\": [\n", " \"1a\"\n", " ],\n", - " \"SMILES\": \"C(c1ccccc1)C(CN(C(=O)c1ccc(Cl)s1)C(Cc1ccccc1)C(=O)O)NC(*)=O\",\n", - " \"E-SMILES\": \"*CC(NC(=O)*)CN(C(=O)C1=CC=C(Cl)S1)C(C*)C(=O)O0:Ph6:R319:Ph\",\n", + " \"SMILES\": \"*C(=O)NC(Cc1ccccc1)CN(C(=O)c1ccc(Cl)s1)C(Cc1ccccc1)C(=O)O\",\n", + " \"E-SMILES\": \"*CC(NC(=O)*)CN(C(=O)C1=CC=C(Cl)S1)C(C*)C(=O)O0:Ph6:R[3]19:Ph\",\n", " \"is_markush\": true\n", " },\n", " {\n", - " \"index\": 28,\n", + " \"index\": 27,\n", " \"molecular_info\": [\n", " \"5a\"\n", " ],\n", @@ -1252,7 +1214,7 @@ " \"is_markush\": false\n", " },\n", " {\n", - " \"index\": 29,\n", + " \"index\": 28,\n", " \"molecular_info\": [\n", " \"手性\"\n", " ],\n", @@ -1261,28 +1223,21 @@ " \"is_markush\": false\n", " },\n", " {\n", - " \"index\": 30,\n", - " \"molecular_info\": [],\n", - " \"SMILES\": \"CC(C)(C)SSC[C@H](CN(C(=O)c1ccc(Cl)s1)[C@@H](Cc1ccccc1)C(=O)O)NC(=O)OCC1c2ccccc2-c2ccccc21\",\n", - " \"E-SMILES\": \"CC(SSC[C@@H](NC(OCC1c2c(cccc2)-c2c1cccc2)=O)CN([C@H](C(O)=O)Cc1ccccc1)C(c1sc(Cl)cc1)=O)(C)C\",\n", - " \"is_markush\": false\n", - " },\n", - " {\n", - " \"index\": 31,\n", + " \"index\": 29,\n", " \"molecular_info\": [\n", " \"手性\"\n", " ],\n", " \"SMILES\": \"O=C(N[C@@H](CS)CN(C(=O)c1ccc(Cl)s1)[C@@H](Cc1ccccc1)C(=O)O)OCC1c2ccccc2-c2ccccc21\",\n", - " \"E-SMILES\": \"O=C(N[C@H](CN([C@H](C(O)=O)Cc1ccccc1)C(c1sc(Cl)cc1)=O)CS)OCC1c2c(cccc2)-c2c1cccc2\",\n", + " \"E-SMILES\": \"O=C(N[C@@H](CS)CN(C(=O)c1ccc(Cl)s1)[C@@H](Cc1ccccc1)C(=O)O)OCC1c2ccccc2-c2ccccc21\",\n", " \"is_markush\": false\n", " },\n", " {\n", - " \"index\": 32,\n", + " \"index\": 30,\n", " \"molecular_info\": [\n", " \"手性\"\n", " ],\n", " \"SMILES\": \"O=C(N[C@@H](Cc1ccccc1)CN(C(=O)c1cnc2ccccc2n1)[C@@H](Cc1ccccc1)C(=O)O)OCC1c2ccccc2-c2ccccc21\",\n", - " \"E-SMILES\": \"O=C(N[C@H](CN([C@H](C(O)=O)Cc1ccccc1)C(c1nc2c(cccc2)nc1)=O)Cc1ccccc1)OCC1c2c(cccc2)-c2c1cccc2\",\n", + " \"E-SMILES\": \"O=C(N[C@@H](Cc1ccccc1)CN(C(=O)c1cnc2ccccc2n1)[C@@H](Cc1ccccc1)C(=O)O)OCC1c2ccccc2-c2ccccc21\",\n", " \"is_markush\": false\n", " }\n", "]\n" @@ -1341,7 +1296,7 @@ " # display(Markdown(mol_group_desc))\n", " print(\"==\" * 50)\n", "\n", - "print(\"\\n提取到的所有 id-smiles 分子对信息：\")\n", + "print(\"\\n已提取到的所有 id-smiles 分子对信息：\")\n", "print(json.dumps(all_pairs, indent=4, ensure_ascii=False))" ] }, diff --git a/skills/UniParser-Tools/SKILL.md b/skills/UniParser-Tools/SKILL.md index ec33dc1..5fcfa89 100644 --- a/skills/UniParser-Tools/SKILL.md +++ b/skills/UniParser-Tools/SKILL.md @@ -1,11 +1,11 @@ --- name: uniparser-tools -description: "Parse PDFs, document images, and public PDF URLs into structured Markdown via UniParser (https://uniparser.dp.tech/)—tables, equations as LaTeX, figures, and reading order. Use when the user wants to parse or extract a document, paper, patent, report, or PDF/image/URL into Markdown. Trigger terms: UniParser, uniparser_tools, 文档解析, PDF解析, 论文解析, 专利解析, PDF转Markdown, 表格提取, 公式识别, 化学分子, scientific paper, layout extraction, dp.tech, document parsing." +description: "Parse PDFs, document images, and supported source URLs into structured Markdown via UniParser (https://uniparser.dp.tech/)—tables, equations as LaTeX, figures, and reading order. Use when the user wants to parse or extract a document, paper, patent, report, or PDF/image/URL into Markdown. Trigger terms: UniParser, uniparser_tools, 文档解析, PDF解析, 论文解析, 专利解析, PDF转Markdown, 表格提取, 公式识别, 化学分子, scientific paper, layout extraction, dp.tech, document parsing." --- # UniParser-Tools Skill -Parse local PDFs, document images, and public PDF URLs into Markdown and structured layout JSON via [UniParser](https://uniparser.dp.tech/). Agents run the bundled CLI scripts—do not hand-write SDK code for the default workflow. +Parse local PDFs, document images, and supported source URLs into Markdown and structured layout JSON via [UniParser](https://uniparser.dp.tech/). Agents run the bundled CLI scripts—do not hand-write SDK code for the default workflow. ## Installation @@ -70,7 +70,7 @@ Prefer environment variables over pasting keys into chat. - Local PDF → `--file-path` - Local image (.png, .jpg, …) → `--image-path` -- Public PDF URL → `--pdf-url` +- Source URL (`http(s)`, `s3`, `oss`, `tos`, or `file`) → `--pdf-url` ```bash python3 scripts/parse_document.py --file-path "/path/to/document.pdf" @@ -126,7 +126,7 @@ On failure, show stderr JSON `error.message`. Do not substitute vision-only read | Wrong input flag | Images require `trigger_snip` | Use `--image-path`, not `--file-path` | | `PARSE_ERROR` | Server `status: error` at trigger / poll / fetch | Read `error.message` and `stage`; match rows above; check `trigger_error.json` / `pages_tree_error.json` / `formatted_error.json` under output dir if present | -**Limits:** large PDFs may take 10–20+ minutes; public service ≤5 concurrent requests ([references/notes.md](./references/notes.md)); `--pdf-url` must be publicly accessible. Recovery via `fetch_by_token.py` must use the **same** input string as the original parse—URL and file path are not interchangeable. +**Limits:** large PDFs may take 10–20+ minutes; public service ≤5 concurrent requests ([references/notes.md](./references/notes.md)); URL schemes and private object-store access depend on the target deployment. Recovery via `fetch_by_token.py` must use the **same** input string as the original parse—URL and file path are not interchangeable. ## Advanced diff --git a/skills/UniParser-Tools/references/api-reference.md b/skills/UniParser-Tools/references/api-reference.md index 7036256..939a5a7 100644 --- a/skills/UniParser-Tools/references/api-reference.md +++ b/skills/UniParser-Tools/references/api-reference.md @@ -6,9 +6,10 @@ |--------|-------------| | `trigger_file(file_path, ...)` | Submit PDF file for parsing | | `trigger_snip(snip_path, ...)` | Submit image for parsing | -| `trigger_url(pdf_url, ...)` | Submit PDF URL for parsing | +| `trigger_url(pdf_url, ...)` | Submit an HTTP/object-storage/local file URL for parsing | | `get_result(token, ...)` | Get raw parsing results | | `get_formatted(token, ...)` | Get formatted output | +| `get_third_party_output(token, ...)` | Get MinerU-compatible output | ### trigger_file() - Async Callback Parameters @@ -28,6 +29,8 @@ result = parser.trigger_file( | `callback_url` | str | `None` | HTTP POST endpoint for completion notification | | `callback_secret` | str | `None` | Shared secret for HMAC-SHA256 payload verification | +All trigger methods also accept the current server options `timeout`, `table_cls`, `inplace_update`, and `preset_layout`. File and image triggers additionally accept `padding_snip`; `admin_debug` is intended for controlled debugging only. + ### trigger_snip() - Async Callback Parameters Same parameters as `trigger_file()` for image parsing. @@ -69,9 +72,20 @@ Same parameters as `trigger_file()` for URL-based parsing. | `pages_dict=True` | Dict organized by pages | Page-level processing | | `pages_tree=True` | Nested tree with parent-child | Structure analysis | | `molecule_source=True` | Include molecule source images | Chemical structure analysis | +| `return_half=True` | Return an available partial result | Polling long-running jobs | **Note:** `get_formatted()` also supports `marginalia=True` to include page headers/footers/numbers. +## URL Sources and Third-Party Output + +`trigger_url()` accepts `http(s)://`, `s3://`, `oss://`, `tos://`, and `file://` sources. PDF and common image formats are supported. + +```python +from uniparser_tools.common.constant import ThirdPartyFormatter + +result = parser.get_third_party_output(token, ThirdPartyFormatter.MinerU) +``` + ## Ordering Methods | Method | Description | Use Case | diff --git a/skills/UniParser-Tools/references/data-classes.md b/skills/UniParser-Tools/references/data-classes.md index d8c6e4c..4285729 100644 --- a/skills/UniParser-Tools/references/data-classes.md +++ b/skills/UniParser-Tools/references/data-classes.md @@ -29,7 +29,7 @@ Available on all result types: | `TextualResult` | `text`, `contents`, `bboxes` | Text content with bounding boxes | | `TabularResult` | `structure` (HTML), `placeholders`, `contents` | Table with HTML structure, has `.df` (DataFrame) | | `EquationResult` | `latex_repr` | Math equation in LaTeX format | -| `MoleculeResult` | `smi`, `caption`, `markush`, `drawing` | Chemical structure (SMILES notation) | +| `MoleculeResult` | `smi`, `esmi`, `caption`, `markush`, `drawing` | Chemical structure with SMILES and extended SMILES | | `ExpressionResult` | `reactions` | Chemical reaction with `reactants`, `conditions`, `products` | | `ChartResult` | `data` | Chart data, has `.df` (DataFrame) | | `FigureResult` | `desc` | Figure with description | diff --git a/skills/UniParser-Tools/references/notes.md b/skills/UniParser-Tools/references/notes.md index 4513e39..b1f9fc5 100644 --- a/skills/UniParser-Tools/references/notes.md +++ b/skills/UniParser-Tools/references/notes.md @@ -14,8 +14,9 @@ import hmac import hashlib - def verify_callback(content, checksum, secret): - expected = hmac.new(secret.encode(), content.encode(), hashlib.sha256).hexdigest() + def verify_callback(body, signature, secret): + checksum = signature.split("=", 1)[-1] + expected = hmac.new(secret.encode(), body, hashlib.sha256).hexdigest() return hmac.compare_digest(expected, checksum) ``` diff --git a/skills/UniParser-Tools/references/patterns.md b/skills/UniParser-Tools/references/patterns.md index ed60131..df37ad5 100644 --- a/skills/UniParser-Tools/references/patterns.md +++ b/skills/UniParser-Tools/references/patterns.md @@ -54,16 +54,15 @@ result = parser.trigger_file( | `callback_url` | Yes | Your server endpoint that accepts POST requests | | `callback_secret` | Yes | Shared secret for HMAC-SHA256 signature verification | -### Callback Payload +### Callback Payload and Signature -When the parsing task completes, the service sends a POST request to `callback_url`: +When parsing completes, the service sends the result as the JSON request body. The signature is not embedded in the JSON; it is sent as `X-UniParser-Signature: sha256=`. ```json { "token": "abc123...", "status": "success", - "content": { ... }, - "checksum": "hmac-sha256-signature" + "result": { ... } } ``` @@ -79,21 +78,20 @@ CALLBACK_SECRET = "your-secret-key" @app.route('/callback', methods=['POST']) def handle_callback(): - data = request.json - content = data['content'] - received_checksum = data['checksum'] - - # Verify signature + body = request.get_data() + signature = request.headers.get('X-UniParser-Signature', '') + received_checksum = signature.split('=', 1)[-1] + expected = hmac.new( CALLBACK_SECRET.encode(), - json.dumps(content).encode(), + body, hashlib.sha256 ).hexdigest() - + if not hmac.compare_digest(received_checksum, expected): return {'error': 'Invalid signature'}, 401 - - # Process the result + + data = request.get_json() token = data['token'] print(f"Task {token} completed!") return {'status': 'ok'} @@ -123,7 +121,7 @@ result = parser.trigger_snip( token = result["token"] ``` -## Pattern 6: Parse PDF from URL +## Pattern 6: Parse from URL or Object Storage ```python result = parser.trigger_url( @@ -133,3 +131,14 @@ result = parser.trigger_url( ) token = result["token"] ``` + +The same method accepts `s3://`, `oss://`, `tos://`, and `file://` sources, including supported image files. + +## Pattern 7: Fetch Partial or MinerU Output + +```python +from uniparser_tools.common.constant import ThirdPartyFormatter + +partial = parser.get_result(token, return_half=True, pages_tree=True) +mineru = parser.get_third_party_output(token, ThirdPartyFormatter.MinerU) +``` diff --git a/skills/UniParser-Tools/scripts/fetch_by_token.py b/skills/UniParser-Tools/scripts/fetch_by_token.py index b277220..4dbb919 100644 --- a/skills/UniParser-Tools/scripts/fetch_by_token.py +++ b/skills/UniParser-Tools/scripts/fetch_by_token.py @@ -46,7 +46,7 @@ def main() -> int: input_group.add_argument("--token", help="UniParser task token (optional; prefer input flags)") input_group.add_argument("--file-path", help="Same local PDF path used in parse_document.py") input_group.add_argument("--image-path", help="Same local image path used in parse_document.py") - input_group.add_argument("--pdf-url", help="Same public PDF URL used in parse_document.py") + input_group.add_argument("--pdf-url", help="Same source URL used in parse_document.py") parser.add_argument( "--output-dir", "-o", diff --git a/skills/UniParser-Tools/scripts/parse_document.py b/skills/UniParser-Tools/scripts/parse_document.py index 5048dbf..7576b13 100644 --- a/skills/UniParser-Tools/scripts/parse_document.py +++ b/skills/UniParser-Tools/scripts/parse_document.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 """ -Parse a local PDF, local image, or public PDF URL with UniParser-Tools. +Parse a local PDF, local image, or supported source URL with UniParser-Tools. Flow: submit → token → poll get_result until success → save pages_tree + Markdown. @@ -48,7 +48,7 @@ def main() -> int: input_group = parser.add_mutually_exclusive_group(required=True) input_group.add_argument("--file-path", help="Local PDF file path") input_group.add_argument("--image-path", help="Local image path (snippet)") - input_group.add_argument("--pdf-url", help="Public PDF URL") + input_group.add_argument("--pdf-url", help="Source URL: http(s), s3, oss, tos, or file") parser.add_argument( "--output-dir", "-o", diff --git a/tests/unit/test_client.py b/tests/unit/test_client.py index 43a9edc..66cb6e5 100644 --- a/tests/unit/test_client.py +++ b/tests/unit/test_client.py @@ -6,10 +6,28 @@ from __future__ import annotations +import json + import pytest from uniparser_tools.api import clients as clients_mod from uniparser_tools.api.clients import UniParserClient +from uniparser_tools.common.constant import ThirdPartyFormatter + + +class _Response: + status_code = 200 + text = "" + reason = "OK" + + def json(self): + return {"status": "success"} + + +class _ErrorResponse(_Response): + status_code = 403 + text = "denied" + reason = "Forbidden" class TestClientConstruction: @@ -22,12 +40,14 @@ def test_rejects_non_http_host(self) -> None: UniParserClient(host="example.com", api_key="k") def test_endpoints_compose_correctly(self) -> None: - c = UniParserClient(host="https://example.com", api_key="k") + c = UniParserClient(host="https://example.com/", api_key="k") + assert c.host == "https://example.com" assert c.trigger_file_endpoint.endswith("/trigger-file-async") assert c.trigger_url_endpoint.endswith("/trigger-url-async") assert c.trigger_snip_endpoint.endswith("/trigger-snip-async") assert c.get_result_endpoint.endswith("/get-result") assert c.get_formatted_endpoint.endswith("/get-formatted") + assert c.get_third_party_output_endpoint.endswith("/get-third-party-output") class TestTokenHelpers: @@ -81,6 +101,16 @@ def test_version_returns_error_dict_on_request_failure(self, monkeypatch) -> Non assert result.get("status") == "error" assert "description" in result + def test_health_uses_requests_reason_for_http_errors(self, monkeypatch) -> None: + monkeypatch.setattr(clients_mod.requests, "get", lambda *args, **kwargs: _ErrorResponse()) + result = UniParserClient(host="https://example.com", api_key="k").health() + assert result == { + "status": "error", + "http_status": 403, + "description": "Forbidden", + "body": "denied", + } + def test_trigger_file_returns_error_dict_on_request_failure(self, monkeypatch, tmp_path) -> None: p = tmp_path / "dummy.pdf" p.write_bytes(b"%PDF-1.4 tiny") @@ -90,3 +120,66 @@ def test_trigger_file_returns_error_dict_on_request_failure(self, monkeypatch, t assert isinstance(result, dict) assert result.get("status") == "error" assert "token" in result + + +class TestClientPayloads: + def test_trigger_file_sends_new_options(self, monkeypatch, tmp_path) -> None: + captured = {} + + def post(*args, **kwargs): + captured.update(kwargs) + return _Response() + + path = tmp_path / "sample.pdf" + path.write_bytes(b"%PDF-1.4") + monkeypatch.setattr(clients_mod.requests, "post", post) + + client = UniParserClient(host="https://example.com", api_key="k") + result = client.trigger_file( + str(path), + admin_debug=True, + timeout=12, + table_cls=True, + padding_snip=False, + inplace_update=True, + preset_layout=[[{"type": "table"}]], + ) + + assert result["status"] == "success" + assert captured["timeout"] == 12 + assert captured["data"]["admin_debug"] is True + assert captured["data"]["table_cls"] is True + assert captured["data"]["padding_snip"] is False + assert captured["data"]["inplace_update"] is True + assert json.loads(captured["data"]["preset_layout"]) == [[{"type": "table"}]] + + def test_result_methods_send_return_half(self, monkeypatch) -> None: + payloads = [] + + def post(*args, **kwargs): + payloads.append(kwargs["json"]) + return _Response() + + monkeypatch.setattr(clients_mod.requests, "post", post) + client = UniParserClient(host="https://example.com", api_key="k") + + client.get_result("token", return_half=True) + client.get_formatted("token", return_half=True) + + assert payloads[0]["return_half"] is True + assert payloads[1]["return_half"] is True + + def test_third_party_output_payload(self, monkeypatch) -> None: + captured = {} + + def post(*args, **kwargs): + captured.update(kwargs) + return _Response() + + monkeypatch.setattr(clients_mod.requests, "post", post) + client = UniParserClient(host="https://example.com", api_key="k") + + result = client.get_third_party_output("token") + + assert result["status"] == "success" + assert captured["json"] == {"token": "token", "formatter": ThirdPartyFormatter.MinerU} diff --git a/tests/unit/test_constant.py b/tests/unit/test_constant.py index ae55e9c..38f7b92 100644 --- a/tests/unit/test_constant.py +++ b/tests/unit/test_constant.py @@ -20,6 +20,7 @@ SemanticType, TableBBoxType, TextualTypes, + ThirdPartyFormatter, to_semantic, ) @@ -41,6 +42,9 @@ def test_ordering_method_membership(self) -> None: assert "xy_cut" in OrderingMethod assert OrderingMethod("gap_tree") is OrderingMethod.GapTree + def test_third_party_formatter(self) -> None: + assert ThirdPartyFormatter.MinerU == "mineru" + class TestIntEnum: def test_table_bbox_type_from_int(self) -> None: diff --git a/tests/unit/test_convert.py b/tests/unit/test_convert.py index 99d46b6..b90b011 100644 --- a/tests/unit/test_convert.py +++ b/tests/unit/test_convert.py @@ -50,7 +50,7 @@ }, TabularResult, ), - ({"markush": False, "smi": "CCO", "caption": "ethanol"}, MoleculeResult), + ({"markush": False, "smi": "CCO", "esmi": "CCO", "caption": "ethanol"}, MoleculeResult), ({"data": "a|b\n1|2"}, ChartResult), ({"desc": "a cat"}, FigureResult), ({"latex_repr": "a+b"}, EquationResult), diff --git a/tests/unit/test_dataclass_results.py b/tests/unit/test_dataclass_results.py index 67da43d..bf0a44e 100644 --- a/tests/unit/test_dataclass_results.py +++ b/tests/unit/test_dataclass_results.py @@ -203,6 +203,14 @@ def test_plain_falls_back_to_caption_for_markush(self) -> None: ) assert r.plain == "*NC(=O)*" + def test_accepts_esmi(self) -> None: + r = MoleculeResult( + **{**ITEM_KWARGS, "type": LayoutType.Molecule}, + smi="CCO", + esmi="CCO", + ) + assert r.esmi == "CCO" + def test_markdown_wraps_in_bold_italic(self) -> None: r = MoleculeResult( **{**ITEM_KWARGS, "type": LayoutType.Molecule}, diff --git a/uniparser_tools/api/clients.py b/uniparser_tools/api/clients.py index c222069..228e563 100644 --- a/uniparser_tools/api/clients.py +++ b/uniparser_tools/api/clients.py @@ -16,6 +16,7 @@ ParseMode, ParseModeTextual, StatusFlag, + ThirdPartyFormatter, ) from uniparser_tools.utils.image import dump_image_base64_str @@ -37,7 +38,13 @@ class TriggerFileData: expression: Union[ParseMode, bool] equation: Union[ParseMode, bool] pages: List[int] = None + admin_debug: bool = False + timeout: int = 1800 + table_cls: bool = False ordering_method: OrderingMethod = OrderingMethod.XYCutExp + padding_snip: bool = True + inplace_update: bool = False + preset_layout: str = "" callback_url: str = None callback_secret: str = None @@ -56,8 +63,13 @@ class TriggerURLData: expression: Union[ParseMode, bool] equation: Union[ParseMode, bool] pages: List[int] = None + admin_debug: bool = False + timeout: int = 1800 + table_cls: bool = False ordering_method: OrderingMethod = OrderingMethod.XYCutExp proxy: str = None + inplace_update: bool = False + preset_layout: str = "" callback_url: str = None callback_secret: str = None @@ -65,6 +77,7 @@ class TriggerURLData: @dataclass class GetResultData: token: str + return_half: bool content: bool objects: bool pages_dict: bool @@ -75,6 +88,7 @@ class GetResultData: @dataclass class GetFormattedData: token: str + return_half: bool content: bool objects: bool pages_dict: bool @@ -90,13 +104,19 @@ class GetFormattedData: marginalia: bool +@dataclass +class GetThirdPartyData: + token: str + formatter: ThirdPartyFormatter + + class UniParserClient: def __init__(self, host: str, api_key: str): assert api_key, "api_key can not be empty" assert host.startswith("http"), "host must start with http or https" self.api_key = api_key self.user = uuid.uuid5(uuid.NAMESPACE_DNS, self.api_key) - self.host = host + self.host = host.rstrip("/") @property def trigger_file_endpoint(self): @@ -118,6 +138,10 @@ def get_result_endpoint(self): def get_formatted_endpoint(self): return f"{self.host}/get-formatted" + @property + def get_third_party_output_endpoint(self): + return f"{self.host}/get-third-party-output" + def to_token(self, task_id: str): token = uuid.uuid5(self.user, task_id).hex return token @@ -138,7 +162,7 @@ def health(self): return { "status": "error", "http_status": response.status_code, - "description": response.reason_phrase, + "description": response.reason, "body": response.text, } try: @@ -159,7 +183,7 @@ def version(self): return { "status": "error", "http_status": response.status_code, - "description": response.reason_phrase, + "description": response.reason, "body": response.text, } try: @@ -184,6 +208,12 @@ def trigger_file( ordering_method: OrderingMethod = OrderingMethod.GapTree, callback_url: str = None, callback_secret: str = None, + admin_debug: bool = False, + timeout: int = 1800, + table_cls: bool = False, + padding_snip: bool = True, + inplace_update: bool = False, + preset_layout: Union[str, list] = "", **kwargs, ): """ @@ -206,16 +236,28 @@ def trigger_file( expression=expression, equation=equation, pages=pages, + admin_debug=admin_debug, + timeout=timeout, + table_cls=table_cls, ordering_method=ordering_method, + padding_snip=padding_snip, + inplace_update=inplace_update, + preset_layout=preset_layout if isinstance(preset_layout, str) else json.dumps(preset_layout), callback_url=callback_url, callback_secret=callback_secret, ) try: headers = {"X-API-Key": self.api_key} - files = {"file": open(file_path, "rb")} data = asdict(trigger_data, dict_factory=int_enum_factory) - response = requests.post(self.trigger_file_endpoint, files=files, data=data, headers=headers) + with open(file_path, "rb") as file: + response = requests.post( + self.trigger_file_endpoint, + files={"file": file}, + data=data, + headers=headers, + timeout=timeout, + ) except Exception: return { "status": StatusFlag.Error, @@ -246,6 +288,12 @@ def trigger_snip( ordering_method: OrderingMethod = OrderingMethod.GapTree, callback_url: str = None, callback_secret: str = None, + admin_debug: bool = False, + timeout: int = 1800, + table_cls: bool = False, + padding_snip: bool = True, + inplace_update: bool = False, + preset_layout: Union[str, list] = "", **kwargs, ): if not token: @@ -263,16 +311,23 @@ def trigger_snip( expression=expression, equation=equation, pages=pages, + admin_debug=admin_debug, + timeout=timeout, + table_cls=table_cls, ordering_method=ordering_method, + padding_snip=padding_snip, + inplace_update=inplace_update, + preset_layout=preset_layout if isinstance(preset_layout, str) else json.dumps(preset_layout), callback_url=callback_url, callback_secret=callback_secret, ) try: headers = {"X-API-Key": self.api_key} - img = dump_image_base64_str(Image.open(snip_path).convert("RGB")) + with Image.open(snip_path) as image: + img = dump_image_base64_str(image.convert("RGB")) data = {"img": img, **asdict(trigger_data, dict_factory=int_enum_factory)} - result = requests.post(self.trigger_snip_endpoint, data=data, headers=headers) + result = requests.post(self.trigger_snip_endpoint, data=data, headers=headers, timeout=timeout) except Exception: return { "status": StatusFlag.Error, @@ -303,6 +358,11 @@ def trigger_url( proxy: str = None, callback_url: str = None, callback_secret: str = None, + admin_debug: bool = False, + timeout: int = 1800, + table_cls: bool = False, + inplace_update: bool = False, + preset_layout: Union[str, list] = "", **kwargs, ): if not token: @@ -321,15 +381,20 @@ def trigger_url( expression=expression, equation=equation, pages=pages, + admin_debug=admin_debug, + timeout=timeout, + table_cls=table_cls, ordering_method=ordering_method, proxy=proxy, + inplace_update=inplace_update, + preset_layout=preset_layout if isinstance(preset_layout, str) else json.dumps(preset_layout), callback_url=callback_url, callback_secret=callback_secret, ) try: headers = {"X-API-Key": self.api_key} data = asdict(trigger_data, dict_factory=int_enum_factory) - result = requests.post(self.trigger_url_endpoint, json=data, headers=headers) + result = requests.post(self.trigger_url_endpoint, json=data, headers=headers, timeout=timeout) except Exception: return { "status": StatusFlag.Error, @@ -350,9 +415,11 @@ def get_result( pages_dict: bool = False, pages_tree: bool = False, molecule_source: bool = False, + return_half: bool = False, ): data = GetResultData( token=token, + return_half=return_half, content=content, objects=objects, pages_dict=pages_dict, @@ -362,7 +429,7 @@ def get_result( try: headers = {"X-API-Key": self.api_key} data = asdict(data, dict_factory=int_enum_factory) - result = requests.post(self.get_result_endpoint, json=data, headers=headers) + result = requests.post(self.get_result_endpoint, json=data, headers=headers, timeout=30) except Exception: return { "status": StatusFlag.Error, @@ -391,9 +458,11 @@ def get_formatted( expression: FormatFlag = FormatFlag.Markdown, equation: FormatFlag = FormatFlag.Markdown, marginalia: bool = False, + return_half: bool = False, ): data = GetFormattedData( token=token, + return_half=return_half, content=content, objects=objects, pages_dict=pages_dict, @@ -411,7 +480,7 @@ def get_formatted( try: headers = {"X-API-Key": self.api_key} data = asdict(data, dict_factory=int_enum_factory) - result = requests.post(self.get_formatted_endpoint, json=data, headers=headers) + result = requests.post(self.get_formatted_endpoint, json=data, headers=headers, timeout=30) except Exception: return { "status": StatusFlag.Error, @@ -423,3 +492,25 @@ def get_formatted( return result.json() except json.decoder.JSONDecodeError: return {"status": StatusFlag.Error, "token": token, "message": result.text} + + def get_third_party_output( + self, + token: str, + formatter: ThirdPartyFormatter = ThirdPartyFormatter.MinerU, + ): + data = GetThirdPartyData(token=token, formatter=formatter) + try: + headers = {"X-API-Key": self.api_key} + data = asdict(data, dict_factory=int_enum_factory) + result = requests.post(self.get_third_party_output_endpoint, json=data, headers=headers, timeout=30) + except Exception: + return { + "status": StatusFlag.Error, + "token": token, + "message": "get third party output failed", + "description": traceback.format_exc(), + } + try: + return result.json() + except json.decoder.JSONDecodeError: + return {"status": StatusFlag.Error, "token": token, "message": result.text} diff --git a/uniparser_tools/common/constant.py b/uniparser_tools/common/constant.py index 68c2656..d79a4a7 100644 --- a/uniparser_tools/common/constant.py +++ b/uniparser_tools/common/constant.py @@ -300,6 +300,7 @@ class ErrorFlag(StrEnum): File_Required = "File is required" # 需要给定文件流 File_Not_Found = "File not found" # 文件不存在 File_Invalid = "File is invalid" # PDF文件无效/页数为0 + PDF_Page_Tree_Broken = "PDF page tree is broken or unsupported" # PDF页树损坏或不受支持 URL_Required = "URL is required" # 需要给定URL Snip_Required = "Snippet is required" # 需要给定文本片段 Snip_Not_Found = "Snippet not found" # 文本片段不存在 @@ -317,6 +318,7 @@ class ErrorFlag(StrEnum): PDF_Pages_Exceeded = "PDF page count exceeds limit" # PDF页数超过限制 File_Type_Not_Allowed = "File type not allowed" # 文件类型不允许 Domain_Not_Allowed = "Domain not allowed" # 域名不允许 + Unsupported_Formatter = "Unsupported formatter" # 不支持的格式化器 class LayoutType(StrEnum): @@ -449,6 +451,10 @@ class SemanticType(StrEnum): Ignore = "ignore" # 忽略 +class ThirdPartyFormatter(StrEnum): + MinerU = "mineru" + + TextualTypes = [ LayoutType.Title, LayoutType.DocumentTitle, diff --git a/uniparser_tools/common/dataclass.py b/uniparser_tools/common/dataclass.py index 1585090..6c77781 100644 --- a/uniparser_tools/common/dataclass.py +++ b/uniparser_tools/common/dataclass.py @@ -764,6 +764,7 @@ class MoleculeResult(SemanticItem): 'markush': True, 'score': 0.9962224612367694, 'smi': '*NC(=O)Nc1nc(*)c2c(*)n[nH]c2c1*', + 'esmi': '', 'sru': False """ @@ -771,6 +772,7 @@ class MoleculeResult(SemanticItem): caption: str = "" markush: bool = False smi: str = "" + esmi: str = "" sru: bool = False drawing: str = "" From e87f4fd58da9104e6785ce91166d87b477aa881c Mon Sep 17 00:00:00 2001 From: jindi <1259666097@qq.com> Date: Tue, 28 Jul 2026 10:48:50 +0800 Subject: [PATCH 2/4] docs: update account information notebook --- playground/app.account_information.ipynb | 905 +---------------------- 1 file changed, 10 insertions(+), 895 deletions(-) diff --git a/playground/app.account_information.ipynb b/playground/app.account_information.ipynb index e55c5b1..a5d7ec4 100644 --- a/playground/app.account_information.ipynb +++ b/playground/app.account_information.ipynb @@ -89,45 +89,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "5ddf0c28", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "账户信息 (GET /users/me)\n", - "============================================================\n", - "{\n", - " \"username\": \"demo_yaoming\",\n", - " \"email\": \"demo_yaoming@guest.example.com\",\n", - " \"is_active\": true,\n", - " \"is_superuser\": false,\n", - " \"id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"created_at\": \"2026-07-17T03:17:44\",\n", - " \"updated_at\": \"2026-07-17T08:58:57\",\n", - " \"status\": \"active\",\n", - " \"rate_limit\": \"50/minute\",\n", - " \"balance\": \"483.05\",\n", - " \"permissions\": {\n", - " \"chart\": 2,\n", - " \"table\": 2,\n", - " \"figure\": 0,\n", - " \"textual\": 2,\n", - " \"equation\": 2,\n", - " \"molecule\": 1,\n", - " \"pdf_pages\": 6000,\n", - " \"expression\": 1,\n", - " \"file_size_mb\": 300\n", - " },\n", - " \"profile\": null\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "profile = get(\"/users/me\")\n", "show(\"账户信息 (GET /users/me)\", profile)\n", @@ -153,33 +118,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "304dd970", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "API Key 列表 (GET /api-keys)\n", - "============================================================\n", - "[\n", - " {\n", - " \"id\": \"52b9db67-64c9-4b42-9383-dc0323d835a7\",\n", - " \"name\": \"common\",\n", - " \"key_identifier\": \"common\",\n", - " \"permissions\": [],\n", - " \"status\": \"active\",\n", - " \"expires_at\": \"2036-07-17T03:17:44\",\n", - " \"last_used_at\": null,\n", - " \"created_at\": \"2026-07-17T03:17:44\"\n", - " }\n", - "]\n" - ] - } - ], + "outputs": [], "source": [ "api_keys = get(\"/api-keys\")\n", "show(\"API Key 列表 (GET /api-keys)\", api_keys)\n", @@ -259,668 +201,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "bf0e19aa", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "用量明细 (GET /billing/usage-records, 共 23 条，仅近 2 周)\n", - "============================================================\n", - "{\n", - " \"items\": [\n", - " {\n", - " \"request_id\": \"8f8ff1e6-c573-4ba6-8945-54ed16d5e83d\",\n", - " \"endpoint\": \"/trigger-file-async\",\n", - " \"method\": \"POST\",\n", - " \"service_type\": \"parser\",\n", - " \"resource_usage\": {\n", - " \"md5\": \"a349fad16277a338a822f777f22b630b\",\n", - " \"page\": 9.0,\n", - " \"token\": \"78f5e7c97e5a5fa3b66962d75f81149d\",\n", - " \"dict_cfg\": \"{'chart': -1, 'equation': 1, 'expression': -1, 'figure': -1, 'molecule': 1, 'table': 1, 'textual': 3}\",\n", - " \"proc_mol\": 0.0,\n", - " \"proc_equa\": 2.0,\n", - " \"proc_expr\": 0.0,\n", - " \"proc_page\": 9.0,\n", - " \"dict_model\": \"{'admin_debug': False, 'inplace_update': False, 'lang': 'unknown', 'ordering_method': 'gap_tree', 'padding_snip': True, 'pages': None, 'table_cls': False, 'timeout': 540}\",\n", - " \"proc_chart\": 9.0,\n", - " \"proc_table\": 9.0,\n", - " \"proc_figure\": 4.0,\n", - " \"proc_textual\": 169.0,\n", - " \"request_start_time\": \"2026-07-17T08:58:55.296017\"\n", - " },\n", - " \"cost_details\": {\n", - " \"total_cost\": 0.45,\n", - " \"base_cost\": {\n", - " \"amount\": 0.0,\n", - " \"description\": \"基础服务费用 (parser)\",\n", - " \"details\": {\n", - " \"service_type\": \"parser\",\n", - " \"pricing_config\": {\n", - " \"base_cost_per_mb\": 0.005,\n", - " \"base_cost_per_page\": 0.01,\n", - " \"processing_time_cost\": 0.001\n", - " }\n", - " }\n", - " },\n", - " \"resource_costs\": {\n", - " \"amount\": 0.0,\n", - " \"description\": \"资源使用费用\",\n", - " \"details\": {\n", - " \"tokens_used\": 0,\n", - " \"file_size_mb\": 0,\n", - " \"pages_processed\": 0,\n", - " \"processing_time\": 0\n", - " }\n", - " },\n", - " \"additional_costs\": {\n", - " \"amount\": 0.0,\n", - " \"description\": \"额外费用\",\n", - " \"details\": {\n", - " \"processing_cost\": 0.45\n", - " }\n", - " }\n", - " },\n", - " \"total_cost\": \"0.45\",\n", - " \"status\": \"success\",\n", - " \"ip_address\": \"39.106.237.255\",\n", - " \"user_agent\": \"python-requests/2.32.4\",\n", - " \"id\": \"b4d4351c-706a-4133-9755-758d47aae4e5\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"organization_id\": null,\n", - " \"api_key_id\": \"52b9db67-64c9-4b42-9383-dc0323d835a7\",\n", - " \"created_at\": \"2026-07-17T08:58:55\",\n", - " \"completed_at\": \"2026-07-17T08:58:57\"\n", - " },\n", - " {\n", - " \"request_id\": \"efa99caf-1c42-4ec0-8b3a-9b47c0272fc8\",\n", - " \"endpoint\": \"/trigger-url-async\",\n", - " \"method\": \"POST\",\n", - " \"service_type\": \"parser\",\n", - " \"resource_usage\": {\n", - " \"md5\": \"a349fad16277a338a822f777f22b630b\",\n", - " \"page\": 9.0,\n", - " \"token\": \"7c766afc569d54a29c1e1dbc9a6559e7\",\n", - " \"dict_cfg\": \"{'chart': -1, 'equation': 2, 'expression': -1, 'figure': -1, 'molecule': 1, 'table': 2, 'textual': 2}\",\n", - 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"execution_count": 12, + "execution_count": null, "id": "498fdae2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "余额 (GET /balance)\n", - "============================================================\n", - "{\n", - " \"balance\": \"483.05\",\n", - " \"currency\": \"CNY\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"username\": \"demo_yaoming\",\n", - " \"status\": \"active\",\n", - " \"bohrium_user_id\": null,\n", - " \"permissions\": {\n", - " \"chart\": 2,\n", - " \"table\": 2,\n", - " \"figure\": 0,\n", - " \"textual\": 2,\n", - " \"equation\": 2,\n", - " \"molecule\": 1,\n", - " \"pdf_pages\": 6000,\n", - " \"expression\": 1,\n", - " \"file_size_mb\": 300\n", - " }\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "balance = get(\"/balance\")\n", "show(\"余额 (GET /balance)\", balance)" @@ -979,149 +233,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "353a858c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "余额流水 (GET /balance/transactions, 共 24 条)\n", - "============================================================\n", - "{\n", - " \"items\": [\n", - " {\n", - " \"transaction_id\": \"e90d44a7-9d92-46e8-a689-d045604dc237\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"admin_id\": null,\n", - " \"amount\": \"-0.45\",\n", - " \"balance_before\": \"483.50\",\n", - " \"balance_after\": \"483.05\",\n", - " \"transaction_type\": \"usage_deduction\",\n", - " \"description\": \"API 使用扣费\",\n", - " \"reference_id\": \"b4d4351c-706a-4133-9755-758d47aae4e5\",\n", - " \"created_at\": \"2026-07-17T08:58:57\"\n", - " },\n", - " {\n", - " \"transaction_id\": \"3b3b8408-94b3-4b6a-84be-6bc0ffe04d37\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"admin_id\": null,\n", - " \"amount\": \"-0.45\",\n", - " \"balance_before\": \"483.95\",\n", - " \"balance_after\": \"483.50\",\n", - " \"transaction_type\": \"usage_deduction\",\n", - " \"description\": \"API 使用扣费\",\n", - " \"reference_id\": \"f939b76e-e402-4458-bc0b-87b5b2c26e52\",\n", - " \"created_at\": \"2026-07-17T08:54:25\"\n", - " },\n", - " {\n", - " \"transaction_id\": \"6d975bd6-1352-424c-a1d3-81abe4ec6d7a\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"admin_id\": null,\n", - " \"amount\": \"-0.05\",\n", - " \"balance_before\": \"484.00\",\n", - " \"balance_after\": \"483.95\",\n", - " \"transaction_type\": \"usage_deduction\",\n", - " \"description\": \"API 使用扣费\",\n", - " \"reference_id\": \"da93e0ad-834f-4696-868d-c28e1b9fad26\",\n", - " \"created_at\": \"2026-07-17T08:54:13\"\n", - " },\n", - " {\n", - " \"transaction_id\": \"d9a55804-aec5-46e3-8c19-27ab34d8a7db\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"admin_id\": null,\n", - " \"amount\": \"-0.45\",\n", - " \"balance_before\": \"484.45\",\n", - " \"balance_after\": \"484.00\",\n", - " \"transaction_type\": \"usage_deduction\",\n", - " \"description\": \"API 使用扣费\",\n", - " \"reference_id\": \"3cd3a9da-221c-4485-9004-688089173b46\",\n", - " \"created_at\": \"2026-07-17T08:53:56\"\n", - " },\n", - " {\n", - " \"transaction_id\": \"c15d2a8f-57c0-4544-980d-658f8dfa44be\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"admin_id\": null,\n", - " \"amount\": \"-1.00\",\n", - " \"balance_before\": \"485.45\",\n", - " \"balance_after\": \"484.45\",\n", - " \"transaction_type\": \"usage_deduction\",\n", - " \"description\": \"API 使用扣费\",\n", - " \"reference_id\": \"55ec5e45-5894-42e8-a3bd-92a75ce9f874\",\n", - " \"created_at\": \"2026-07-17T05:58:41\"\n", - " },\n", - " {\n", - " \"transaction_id\": \"dd80057b-ca8a-47b9-9678-2ccc35840226\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"admin_id\": null,\n", - " \"amount\": \"-1.70\",\n", - " \"balance_before\": \"487.15\",\n", - " \"balance_after\": \"485.45\",\n", - " \"transaction_type\": \"usage_deduction\",\n", - " \"description\": \"API 使用扣费\",\n", - " \"reference_id\": \"6207ac03-e874-416a-81a2-727db97d40bd\",\n", - " \"created_at\": \"2026-07-17T05:58:14\"\n", - " },\n", - " {\n", - " \"transaction_id\": \"aee16168-88f9-4cd5-a295-0d4aeeb690cb\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"admin_id\": null,\n", - " \"amount\": \"-1.00\",\n", - " \"balance_before\": \"488.15\",\n", - " \"balance_after\": \"487.15\",\n", - " \"transaction_type\": \"usage_deduction\",\n", - " \"description\": \"API 使用扣费\",\n", - " \"reference_id\": \"43491238-138a-42c9-b968-b8e461f84082\",\n", - " \"created_at\": \"2026-07-17T05:55:39\"\n", - " },\n", - " {\n", - " \"transaction_id\": \"7dba1da7-5763-4f3e-9c61-8bd1f6e92b31\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"admin_id\": null,\n", - " \"amount\": \"-1.70\",\n", - " \"balance_before\": \"489.85\",\n", - " \"balance_after\": \"488.15\",\n", - " \"transaction_type\": \"usage_deduction\",\n", - " \"description\": \"API 使用扣费\",\n", - " \"reference_id\": \"26b1ddf8-d3ff-43e3-8441-a33d0a13a11c\",\n", - " \"created_at\": \"2026-07-17T05:54:20\"\n", - " },\n", - " {\n", - " \"transaction_id\": \"3dea591e-5583-49fd-a547-f46b1ea2a95a\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"admin_id\": null,\n", - " \"amount\": \"-1.00\",\n", - " \"balance_before\": \"490.85\",\n", - " \"balance_after\": \"489.85\",\n", - " \"transaction_type\": \"usage_deduction\",\n", - " \"description\": \"API 使用扣费\",\n", - " \"reference_id\": \"e14bf3cd-ca27-49ec-9578-57078aa58feb\",\n", - " \"created_at\": \"2026-07-17T04:09:22\"\n", - " },\n", - " {\n", - " \"transaction_id\": \"6b18f95d-ec80-46f0-91e7-4225c3fd3be8\",\n", - " \"user_id\": \"4765260b-4f96-48ba-93f3-7fdab33cecf8\",\n", - " \"admin_id\": null,\n", - " \"amount\": \"-0.45\",\n", - " \"balance_before\": \"491.30\",\n", - " \"balance_after\": \"490.85\",\n", - " \"transaction_type\": \"usage_deduction\",\n", - " \"description\": \"API 使用扣费\",\n", - " \"reference_id\": \"d691ee42-b023-4c85-81e4-a57d76e4345c\",\n", - " \"created_at\": \"2026-07-17T04:08:24\"\n", - " }\n", - " ],\n", - " \"total\": 24,\n", - " \"page\": 1,\n", - " \"size\": 10,\n", - " \"pages\": 3\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "page, size = 1, 10\n", "txns = get(\"/balance/transactions\", page=page, size=size)\n", From 04e8ade9ae2aef6cb90f1618494aa8fcadfc8019 Mon Sep 17 00:00:00 2001 From: jindi <1259666097@qq.com> Date: Tue, 28 Jul 2026 10:56:06 +0800 Subject: [PATCH 3/4] style: apply ruff formatting --- README.md | 26 +++++++++--------- .../references/api-reference.md | 6 ++--- .../references/data-classes.md | 2 +- skills/UniParser-Tools/references/notes.md | 1 + skills/UniParser-Tools/references/patterns.md | 27 +++++++++---------- .../UniParser-Tools/references/utilities.md | 26 +++++++++--------- 6 files changed, 42 insertions(+), 46 deletions(-) diff --git a/README.md b/README.md index d250cdd..66c95d6 100644 --- a/README.md +++ b/README.md @@ -101,6 +101,7 @@ uniparser parse report.pdf ```python import os + parser = UniParserClient( host="https://uniparser.dp.tech/", api_key=os.getenv("UNIPARSER_API_KEY"), @@ -152,13 +153,10 @@ import os from uniparser_tools.api.clients import UniParserClient # 设置 API 密钥 -api_key = os.getenv('UNIPARSER_API_KEY') +api_key = os.getenv("UNIPARSER_API_KEY") # 初始化客户端 -parser = UniParserClient( - host="https://uniparser.dp.tech/", - api_key=api_key -) +parser = UniParserClient(host="https://uniparser.dp.tech/", api_key=api_key) ``` ### 2. 解析 PDF 文件（科学文献推荐默认） @@ -170,12 +168,12 @@ from uniparser_tools.common.constant import ParseMode, ParseModeTextual result = parser.trigger_file( file_path="./example.pdf", textual=ParseModeTextual.OCRHighQuality, # high quality - equation=ParseMode.OCRHighQuality, # high quality - table=ParseMode.OCRHighQuality, # high quality - chart=ParseMode.DumpBase64, # original image base64 - figure=ParseMode.DumpBase64, # original image base64 - expression=ParseMode.DumpBase64, # original image base64 - molecule=ParseMode.OCRFast, # fast + equation=ParseMode.OCRHighQuality, # high quality + table=ParseMode.OCRHighQuality, # high quality + chart=ParseMode.DumpBase64, # original image base64 + figure=ParseMode.DumpBase64, # original image base64 + expression=ParseMode.DumpBase64, # original image base64 + molecule=ParseMode.OCRFast, # fast ) if result["status"] == "success": @@ -346,9 +344,9 @@ from uniparser_tools.common.constant import FormatFlag result = parser.get_formatted( token, content=True, - textual=FormatFlag.Markdown, # 文本使用 Markdown - table=FormatFlag.Html, # 表格使用 HTML - equation=FormatFlag.Latex, # 公式使用 LaTeX + textual=FormatFlag.Markdown, # 文本使用 Markdown + table=FormatFlag.Html, # 表格使用 HTML + equation=FormatFlag.Latex, # 公式使用 LaTeX ) if result["status"] == "success": diff --git a/skills/UniParser-Tools/references/api-reference.md b/skills/UniParser-Tools/references/api-reference.md index 939a5a7..41f7793 100644 --- a/skills/UniParser-Tools/references/api-reference.md +++ b/skills/UniParser-Tools/references/api-reference.md @@ -16,9 +16,9 @@ ```python result = parser.trigger_file( file_path="./document.pdf", - sync=False, # Required for async mode - callback_url="https://...", # Your callback endpoint - callback_secret="your-secret", # For signature verification + sync=False, # Required for async mode + callback_url="https://...", # Your callback endpoint + callback_secret="your-secret", # For signature verification # ... other parse mode parameters ) ``` diff --git a/skills/UniParser-Tools/references/data-classes.md b/skills/UniParser-Tools/references/data-classes.md index 4285729..c397aea 100644 --- a/skills/UniParser-Tools/references/data-classes.md +++ b/skills/UniParser-Tools/references/data-classes.md @@ -54,6 +54,6 @@ for page in pages_tree: print(f"LaTeX: {item.latex}") # For tables, access DataFrame - if hasattr(item, 'df'): + if hasattr(item, "df"): print(f"DataFrame:\n{item.df}") ``` diff --git a/skills/UniParser-Tools/references/notes.md b/skills/UniParser-Tools/references/notes.md index b1f9fc5..7b64ef4 100644 --- a/skills/UniParser-Tools/references/notes.md +++ b/skills/UniParser-Tools/references/notes.md @@ -14,6 +14,7 @@ import hmac import hashlib + def verify_callback(body, signature, secret): checksum = signature.split("=", 1)[-1] expected = hmac.new(secret.encode(), body, hashlib.sha256).hexdigest() diff --git a/skills/UniParser-Tools/references/patterns.md b/skills/UniParser-Tools/references/patterns.md index df37ad5..52079fa 100644 --- a/skills/UniParser-Tools/references/patterns.md +++ b/skills/UniParser-Tools/references/patterns.md @@ -76,25 +76,22 @@ from flask import Flask, request app = Flask(__name__) CALLBACK_SECRET = "your-secret-key" -@app.route('/callback', methods=['POST']) + +@app.route("/callback", methods=["POST"]) def handle_callback(): body = request.get_data() - signature = request.headers.get('X-UniParser-Signature', '') - received_checksum = signature.split('=', 1)[-1] + signature = request.headers.get("X-UniParser-Signature", "") + received_checksum = signature.split("=", 1)[-1] - expected = hmac.new( - CALLBACK_SECRET.encode(), - body, - hashlib.sha256 - ).hexdigest() + expected = hmac.new(CALLBACK_SECRET.encode(), body, hashlib.sha256).hexdigest() if not hmac.compare_digest(received_checksum, expected): - return {'error': 'Invalid signature'}, 401 + return {"error": "Invalid signature"}, 401 data = request.get_json() - token = data['token'] + token = data["token"] print(f"Task {token} completed!") - return {'status': 'ok'} + return {"status": "ok"} ``` ## Pattern 4: Mixed Format Output @@ -103,10 +100,10 @@ def handle_callback(): result = parser.get_formatted( token, content=True, - textual=FormatFlag.Markdown, # Text as Markdown - table=FormatFlag.Html, # Tables as HTML - equation=FormatFlag.Latex, # Equations as LaTeX - figure=FormatFlag.Markdown, # Figures as Markdown img + textual=FormatFlag.Markdown, # Text as Markdown + table=FormatFlag.Html, # Tables as HTML + equation=FormatFlag.Latex, # Equations as LaTeX + figure=FormatFlag.Markdown, # Figures as Markdown img ) ``` diff --git a/skills/UniParser-Tools/references/utilities.md b/skills/UniParser-Tools/references/utilities.md index 2b90562..530d0e8 100644 --- a/skills/UniParser-Tools/references/utilities.md +++ b/skills/UniParser-Tools/references/utilities.md @@ -25,22 +25,22 @@ clean_text = clean_scientific_text(text, strict=False) from uniparser_tools.utils.bbox import BBox, Point # BBox properties -bbox.area # Area -bbox.width # Width -bbox.height # Height -bbox.tl # Top-left point -bbox.br # Bottom-right point -bbox.ctr # Center point -bbox.xyxy # (x1, y1, x2, y2) tuple -bbox.xywh # (x, y, w, h) tuple +bbox.area # Area +bbox.width # Width +bbox.height # Height +bbox.tl # Top-left point +bbox.br # Bottom-right point +bbox.ctr # Center point +bbox.xyxy # (x1, y1, x2, y2) tuple +bbox.xywh # (x, y, w, h) tuple # BBox operations -bbox.iou(other) # Intersection over Union -bbox.iof(other) # Intersection over Foreground +bbox.iou(other) # Intersection over Union +bbox.iof(other) # Intersection over Foreground bbox.intersection(other) # Intersection box -bbox.union(other) # Union box -bbox.expand(pix, wh) # Expand by pixels -bbox.shrink(pix, wh) # Shrink by pixels +bbox.union(other) # Union box +bbox.expand(pix, wh) # Expand by pixels +bbox.shrink(pix, wh) # Shrink by pixels ``` ## Text Processing From a1cfc8a687dcf05cd40d7d6c384b9037a03e0255 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 28 Jul 2026 04:52:16 +0000 Subject: [PATCH 4/4] fix: redact task token in crop notebook output --- playground/app.crop_images.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/playground/app.crop_images.ipynb b/playground/app.crop_images.ipynb index df24ec5..55ff35e 100644 --- a/playground/app.crop_images.ipynb +++ b/playground/app.crop_images.ipynb @@ -101,7 +101,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务提交成功, Token: 78f5e7c97e5a5fa3b66962d75f81149d\n" + "任务提交成功, Token: \n" ] } ],