From f2e683401d26f9cdac696536bd7466fd3f43f7fb Mon Sep 17 00:00:00 2001 From: ViktorAxelsen <1009254799@qq.com> Date: Mon, 14 Oct 2024 21:29:26 +0800 Subject: [PATCH] sync --- README.md | 102 +- data_process.py | 204 ++ docs/index.html | 244 +++ docs/static/css/bulma-carousel.min.css | 1 + docs/static/css/bulma-slider.min.css | 1 + docs/static/css/bulma.css.map.txt | 1 + docs/static/css/bulma.min.css | 1 + docs/static/css/fontawesome.all.min.css | 5 + docs/static/css/index.css | 157 ++ docs/static/figs/RAG.png | Bin 0 -> 49951 bytes docs/static/js/bulma-carousel.js | 2371 +++++++++++++++++++++++ docs/static/js/bulma-carousel.min.js | 1 + docs/static/js/bulma-slider.js | 461 +++++ docs/static/js/bulma-slider.min.js | 1 + docs/static/js/fontawesome.all.min.js | 5 + docs/static/js/index.js | 78 + eval.py | 107 + figures/.keep | 0 figures/model.png | Bin 0 -> 551652 bytes graph_construction.py | 216 +++ index.html | 1 - prompt_pool.py | 143 ++ retrieval.py | 90 + sum_eval.py | 92 + train.py | 248 +++ training_preparation.py | 108 ++ utils.py | 125 ++ 27 files changed, 4747 insertions(+), 16 deletions(-) create mode 100644 data_process.py create mode 100644 docs/index.html create mode 100644 docs/static/css/bulma-carousel.min.css create mode 100644 docs/static/css/bulma-slider.min.css create mode 100644 docs/static/css/bulma.css.map.txt create mode 100644 docs/static/css/bulma.min.css create mode 100644 docs/static/css/fontawesome.all.min.css create mode 100644 docs/static/css/index.css create mode 100644 docs/static/figs/RAG.png create mode 100644 docs/static/js/bulma-carousel.js create mode 100644 docs/static/js/bulma-carousel.min.js create mode 100644 docs/static/js/bulma-slider.js create mode 100644 docs/static/js/bulma-slider.min.js create mode 100644 docs/static/js/fontawesome.all.min.js create mode 100644 docs/static/js/index.js create mode 100644 eval.py delete mode 100644 figures/.keep create mode 100644 figures/model.png create mode 100644 graph_construction.py delete mode 100644 index.html create mode 100644 prompt_pool.py create mode 100644 retrieval.py create mode 100644 sum_eval.py create mode 100644 train.py create mode 100644 training_preparation.py create mode 100644 utils.py diff --git a/README.md b/README.md index 78eddf0..49d8ae4 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,6 @@ -![Method](./figures/logo.png) - # Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs - -

Build @@ -12,9 +8,9 @@ Build - + License @@ -33,37 +29,113 @@

🌐 Project Page | - 📜 arXiv | - 📮 Twitter Post + 📜 arXiv +

+![Method](./figures/model.png) + ## News -**[2024.10.1x]** 🌟Release GoR + +**[2024.10.1x]** 🌟 GoR is released. + +## 📌Preliminary -## 📌Environment Setup +### Environment Setup ```bash # python==3.10 pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113 pip install dgl==1.0.0+cu113 -f https://data.dgl.ai/wheels/cu113/repo.html -pip install scikit-learn +pip install openai==0.28 +pip install pandas +pip install langchain +pip install langchain-core +pip install langchain-community +pip install langchain-experimental +pip install tiktoken +pip install tqdm +pip install bert_score +pip install rouge_score +pip install networkx +pip install faiss-gpu +pip install transformers ``` +### Dataset Preparation + +[QMSum](https://github.com/Yale-LILY/QMSum) +[WCEP](https://huggingface.co/datasets/ccdv/WCEP-10) +[Booksum](https://huggingface.co/datasets/kmfoda/booksum) +[GovReport](https://huggingface.co/datasets/ccdv/govreport-summarization/tree/refs%2Fconvert%2Fparquet/document) +[SQuALITY](https://github.com/nyu-mll/SQuALITY) + + +Save the downloaded files in the `./data/[DATASET_NAME]` folder. -## ⭐Experiments > \[!IMPORTANT\] > -> Before running the experiment, please configure your API KEY +> Before running the experiment, please configure your API KEY in `"get_llm_response_via_api"` in `utils.py` + + + +## ⭐Experiments + + + +### Query Simulation and Graph Construction + +Generate simulated queries and construct graphs. The constructed graphs are saved in the `./graph` folder. + +```bash +# DATASET Choices: qmsum, wcep, booksum, govreport, squality +# Training Set +python graph_construction.py --cuda 0 --dataset [DATASET] --train +# Test Set +python graph_construction.py --cuda 0 --dataset [DATASET] +``` +### Training Preparation + +Pre-compute BERTScore and save training data in the `./training_data` folder. + + + +```bash +# DATASET Choices: qmsum, wcep, booksum, govreport, squality +python training_preparation.py --cuda 0 --dataset [DATASET] +``` + + + +### Training + + +```bash +# DATASET Choices: qmsum, wcep, booksum, govreport, squality +python train.py --cuda 0 --dataset [DATASET] +``` + + +### Evaluation + + +```bash +# DATASET Choices: qmsum, wcep, booksum, govreport, squality +# Generate summary results +python eval.py --cuda 0 --dataset [DATASET] +# Evaluation +python sum_eval.py --cuda 0 --file_name ./result/[DATASET].json +``` @@ -87,7 +159,7 @@ x ``` - + \ No newline at end of file diff --git a/data_process.py b/data_process.py new file mode 100644 index 0000000..d11183a --- /dev/null +++ b/data_process.py @@ -0,0 +1,204 @@ +import json +import pandas as pd + + +def clean_data(text): + text = text.replace('{ vocalsound } ', '') + text = text.replace('{ disfmarker } ', '') + text = text.replace('a_m_i_', 'ami') + text = text.replace('l_c_d_', 'lcd') + text = text.replace('p_m_s', 'pms') + text = text.replace('t_v_', 'tv') + text = text.replace('{ pause } ', '') + text = text.replace('{ nonvocalsound } ', '') + text = text.replace('{ gap } ', '') + + return text + + +def qm_process_data(train=True): + ret = [] + data = [] + with open("./data/QMSum/data/ALL/jsonl/{}.jsonl".format("train" if train else "test"), 'r') as file: + for line in file: + data.append(json.loads(line)) + + for sample in data: + ret_sample = dict() + ret_sample["topic_list"] = sample['topic_list'] + ret_sample["general_query_list"] = sample['general_query_list'] + all_transcripts = "\n".join( + ["Speaker: " + i["speaker"] + "\n" + "Content: " + i["content"] for i in sample['meeting_transcripts']]) + ret_sample["meeting_transcripts"] = clean_data(all_transcripts) + ret.append(ret_sample) + + return ret + + +def booksum_process_data(train=True): + ret = [] + if train: + data = pd.read_csv('./data/Booksum/train.csv') + else: + data = pd.read_csv('./data/Booksum/test.csv') + + for index, row in data.iterrows(): + # Filter out too short text for long-context summarization + if row["chapter_length"] <= 8000: + continue + ret.append(row) + + return ret + + +def wcep_process_data(train=True): + ret = [] + data = [] + with open('./data/WCEP/{}.txt'.format("train" if train else "test"), 'r') as file: + lines = file.readlines() + for line in lines: + data.append(json.loads(line)) + + for row in data: + word_num = len(" ".join(row["document"]).split()) + # Filter out too short text for long-context summarization + if word_num <= 6000: + continue + ret.append(row) + + return ret + + +def gov_process_data(train=True): + ret = [] + if train: + data1 = pd.read_parquet('./data/GovReport/document/train-00000-of-00002.parquet') + data2 = pd.read_parquet('./data/GovReport/document/train-00001-of-00002.parquet') + data = pd.concat([data1, data2]) + else: + data = pd.read_parquet('./data/GovReport/document/test-00000-of-00001.parquet') + + for index, row in data.iterrows(): + word_num = len(row["report"].split()) + # Filter out too short text for long-context summarization + if word_num <= 8000: + continue + ret.append(row) + + return ret + + +def squ_process_data(train=True): + ret = [] + data = [] + if train: + # Expand the training set + with open("./data/SQuALITY/data/v1-3/txt/train.jsonl", 'r') as file: + for line in file: + data.append(json.loads(line)) + with open("./data/SQuALITY/data/v1-3/txt/dev.jsonl", 'r') as file: + for line in file: + data.append(json.loads(line)) + cnt = 0 + with open("./data/SQuALITY/data/v1-3/txt/test.jsonl", 'r') as file: + for line in file: + data.append(json.loads(line)) + cnt += 1 + if cnt == 25: + break + else: + # Ensure that the test set does not overlap with the training set + cnt = 0 + with open("./data/SQuALITY/data/v1-3/txt/test.jsonl", 'r') as file: + for line in file: + if cnt < 25: + cnt += 1 + continue + data.append(json.loads(line)) + + for sample in data: + ret.append(sample) + + return ret + + +def get_processed_data(dataset, train=True): + if dataset == "qmsum": + data = qm_process_data(train=train) + elif dataset == "wcep": + data = wcep_process_data(train=train) + elif dataset == "booksum": + data = booksum_process_data(train=train) + elif dataset == "govreport": + data = gov_process_data(train=train) + elif dataset == "squality": + data = squ_process_data(train=train) + else: + raise Exception("Dataset Error") + + return data + + +def split_corpus_by_doc(dataset, sample, text_splitter): + chunk_list = [] + if dataset == "qmsum": + doc_list = [sample["meeting_transcripts"]] + elif dataset == "wcep": + doc_list = sample["document"] + elif dataset == "booksum": + doc_list = [sample["chapter"]] + elif dataset == "govreport": + doc_list = [sample["report"]] + elif dataset == "squality": + doc_list = [sample["document"]] + else: + raise Exception("Dataset Error") + + for doc in doc_list: + chunk_list.extend(text_splitter.split_text(doc)) + + return chunk_list + + +def eval_data_generation(dataset, sample): + ret = [] + if dataset == "qmsum": + all_topic = ", ".join([i["topic"] for i in sample["topic_list"]]) + for test_query in sample["general_query_list"]: + data = dict() + data["rag_query"] = test_query["query"] + " The topic list of the meeting transcript is: " + all_topic + data["query"] = test_query["query"] + data["summary"] = test_query["answer"] + ret.append(data) + elif dataset == "wcep": + data = dict() + data["rag_query"] = "Summarize the contents of this news event." + data["query"] = "Summarize the contents of this news event." + data["summary"] = sample["summary"] + ret.append(data) + elif dataset == "booksum": + data = dict() + data["rag_query"] = "Summarize the contents of this story." + data["query"] = "Summarize the contents of this story." + data["summary"] = sample["summary_text"] + ret.append(data) + elif dataset == "govreport": + data = dict() + data["rag_query"] = "Summarize the contents of this report." + data["query"] = "Summarize the contents of this report." + data["summary"] = sample["summary"] + ret.append(data) + elif dataset == "squality": + data = dict() + data["rag_query"] = sample["questions"][0]["question_text"] + data["query"] = sample["questions"][0]["question_text"] + data["summary"] = [i["response_text"] for i in sample["questions"][0]["responses"]] + ret.append(data) + else: + raise Exception("Dataset Error") + + return ret + + +if __name__ == '__main__': + pass diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..37fd903 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,244 @@ + + + + + + + + Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs + + + + + + + + + + + + + + + + + + + + + + +

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Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs

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+ + Haozhen Zhang1, + + Tao Feng2, + + Jiaxuan You2, + +
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+ 1Tsinghua University, + 2University of Illinois at Urbana-Champaign +
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Abstract

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+ Retrieval-augmented generation (RAG) has revitalized Large Language Models (LLMs) by injecting non-parametric factual knowledge. + Compared with long-context LLMs, RAG is considered an effective summarization tool in a more concise and lightweight manner, which can interact with LLMs multiple times using diverse queries to get comprehensive responses. + However, the LLM-generated historical responses, which contain potentially insightful information, are largely neglected and discarded by existing approaches, leading to suboptimal results. + In this paper, we propose graph of records (GoR), which leverages historical responses generated by LLMs to enhance RAG for long-context global summarization. + Inspired by the retrieve-then-generate paradigm of RAG, we construct a graph by creating an edge between the retrieved text chunks and the corresponding LLM-generated response. + To further uncover the sophisticated correlations between them, GoR further features a graph neural network and an elaborately designed BERTScore-based objective for self-supervised model training, enabling seamless supervision signal backpropagation between reference summaries and node embeddings. + We comprehensively compare GoR with 12 baselines on four long-context summarization datasets, and the results indicate that our proposed method reaches the best performance. + Extensive experiments further demonstrate the effectiveness of GoR. +

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Graph of Records (GoR)

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Model Figure

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Experiments

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Results

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BibTeX

+ 
@article{GoR,
+  author    = {Haozhen Zhang and Tao Feng and Jiaxuan You*},
+  title     = {Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs},
+  journal   = {arXiv preprint arXiv:xxx},
+  year      = {2024},
+}
+
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+})(typeof self !== 'undefined' ? self : this, function() { +return /******/ (function(modules) { // webpackBootstrap +/******/ // The module cache +/******/ var installedModules = {}; +/******/ +/******/ // The require function +/******/ function __webpack_require__(moduleId) { +/******/ +/******/ // Check if module is in cache +/******/ if(installedModules[moduleId]) { +/******/ return installedModules[moduleId].exports; +/******/ } +/******/ // Create a new module (and put it into the cache) +/******/ var module = installedModules[moduleId] = { +/******/ i: moduleId, +/******/ l: false, +/******/ exports: {} +/******/ }; +/******/ +/******/ // Execute the module function +/******/ modules[moduleId].call(module.exports, module, module.exports, __webpack_require__); +/******/ +/******/ // Flag the module as loaded +/******/ module.l = true; +/******/ +/******/ // Return the exports of the module +/******/ return module.exports; +/******/ } +/******/ +/******/ +/******/ // expose the modules object (__webpack_modules__) +/******/ __webpack_require__.m = modules; +/******/ +/******/ // expose the module cache +/******/ __webpack_require__.c = installedModules; +/******/ +/******/ // define getter function for harmony exports +/******/ __webpack_require__.d = function(exports, name, getter) { +/******/ if(!__webpack_require__.o(exports, name)) { +/******/ Object.defineProperty(exports, name, { +/******/ configurable: false, +/******/ enumerable: true, +/******/ get: getter +/******/ }); +/******/ } +/******/ }; +/******/ +/******/ // getDefaultExport function for compatibility with non-harmony modules +/******/ __webpack_require__.n = function(module) { +/******/ var getter = module && module.__esModule ? +/******/ function getDefault() { return module['default']; } : +/******/ function getModuleExports() { return module; }; +/******/ __webpack_require__.d(getter, 'a', getter); +/******/ return getter; +/******/ }; +/******/ +/******/ // Object.prototype.hasOwnProperty.call +/******/ __webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); }; +/******/ +/******/ // __webpack_public_path__ +/******/ __webpack_require__.p = ""; +/******/ +/******/ // Load entry module and return exports +/******/ return __webpack_require__(__webpack_require__.s = 5); +/******/ }) +/************************************************************************/ +/******/ ([ +/* 0 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export addClasses */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "d", function() { return removeClasses; }); +/* unused harmony export show */ +/* unused harmony export hide */ +/* unused harmony export offset */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "e", function() { return width; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return height; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return outerHeight; }); +/* unused harmony export outerWidth */ +/* unused harmony export position */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return css; }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__type__ = __webpack_require__(2); + + +var addClasses = function addClasses(element, classes) { + classes = Array.isArray(classes) ? classes : classes.split(' '); + classes.forEach(function (cls) { + element.classList.add(cls); + }); +}; + +var removeClasses = function removeClasses(element, classes) { + classes = Array.isArray(classes) ? classes : classes.split(' '); + classes.forEach(function (cls) { + element.classList.remove(cls); + }); +}; + +var show = function show(elements) { + elements = Array.isArray(elements) ? elements : [elements]; + elements.forEach(function (element) { + element.style.display = ''; + }); +}; + +var hide = function hide(elements) { + elements = Array.isArray(elements) ? elements : [elements]; + elements.forEach(function (element) { + element.style.display = 'none'; + }); +}; + +var offset = function offset(element) { + var rect = element.getBoundingClientRect(); + return { + top: rect.top + document.body.scrollTop, + left: rect.left + document.body.scrollLeft + }; +}; + +// returns an element's width +var width = function width(element) { + return element.getBoundingClientRect().width || element.offsetWidth; +}; +// returns an element's height +var height = function height(element) { + return element.getBoundingClientRect().height || element.offsetHeight; +}; + +var outerHeight = function outerHeight(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var height = element.offsetHeight; + if (withMargin) { + var style = window.getComputedStyle(element); + height += parseInt(style.marginTop) + parseInt(style.marginBottom); + } + return height; +}; + +var outerWidth = function outerWidth(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var width = element.offsetWidth; + if (withMargin) { + var style = window.getComputedStyle(element); + width += parseInt(style.marginLeft) + parseInt(style.marginRight); + } + return width; +}; + +var position = function position(element) { + return { + left: element.offsetLeft, + top: element.offsetTop + }; +}; + +var css = function css(element, obj) { + if (!obj) { + return window.getComputedStyle(element); + } + if (Object(__WEBPACK_IMPORTED_MODULE_0__type__["b" /* isObject */])(obj)) { + var style = ''; + Object.keys(obj).forEach(function (key) { + style += key + ': ' + obj[key] + ';'; + }); + + element.style.cssText += style; + } +}; + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (immutable) */ __webpack_exports__["a"] = detectSupportsPassive; +function detectSupportsPassive() { + var supportsPassive = false; + + try { + var opts = Object.defineProperty({}, 'passive', { + get: function get() { + supportsPassive = true; + } + }); + + window.addEventListener('testPassive', null, opts); + window.removeEventListener('testPassive', null, opts); + } catch (e) {} + + return supportsPassive; +} + +/***/ }), +/* 2 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isFunction; }); +/* unused harmony export isNumber */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return isString; }); +/* unused harmony export isDate */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return isObject; }); +/* unused harmony export isEmptyObject */ +/* unused harmony export isNode */ +/* unused harmony export isVideo */ +/* unused harmony export isHTML5 */ +/* unused harmony export isIFrame */ +/* unused harmony export isYoutube */ +/* unused harmony export isVimeo */ +var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; + +var isFunction = function isFunction(unknown) { + return typeof unknown === 'function'; +}; +var isNumber = function isNumber(unknown) { + return typeof unknown === "number"; +}; +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; +var isDate = function isDate(unknown) { + return (Object.prototype.toString.call(unknown) === '[object Date]' || unknown instanceof Date) && !isNaN(unknown.valueOf()); +}; +var isObject = function isObject(unknown) { + return (typeof unknown === 'function' || (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && !!unknown) && !Array.isArray(unknown); +}; +var isEmptyObject = function isEmptyObject(unknown) { + for (var name in unknown) { + if (unknown.hasOwnProperty(name)) { + return false; + } + } + return true; +}; + +var isNode = function isNode(unknown) { + return !!(unknown && unknown.nodeType === HTMLElement | SVGElement); +}; +var isVideo = function isVideo(unknown) { + return isYoutube(unknown) || isVimeo(unknown) || isHTML5(unknown); +}; +var isHTML5 = function isHTML5(unknown) { + return isNode(unknown) && unknown.tagName === 'VIDEO'; +}; +var isIFrame = function isIFrame(unknown) { + return isNode(unknown) && unknown.tagName === 'IFRAME'; +}; +var isYoutube = function isYoutube(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/\/\/.*?youtube(-nocookie)?\.[a-z]+\/(watch\?v=[^&\s]+|embed)|youtu\.be\/.*/); +}; +var isVimeo = function isVimeo(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/vimeo\.com\/video\/.*/); +}; + +/***/ }), +/* 3 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var events = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this.events = new Map(events); + } + + _createClass(EventEmitter, [{ + key: "on", + value: function on(name, cb) { + var _this = this; + + this.events.set(name, [].concat(_toConsumableArray(this.events.has(name) ? this.events.get(name) : []), [cb])); + + return function () { + return _this.events.set(name, _this.events.get(name).filter(function (fn) { + return fn !== cb; + })); + }; + } + }, { + key: "emit", + value: function emit(name) { + for (var _len = arguments.length, args = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) { + args[_key - 1] = arguments[_key]; + } + + return this.events.has(name) && this.events.get(name).map(function (fn) { + return fn.apply(undefined, args); + }); + } + }]); + + return EventEmitter; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (EventEmitter); + +/***/ }), +/* 4 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Coordinate = function () { + function Coordinate() { + var x = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + var y = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0; + + _classCallCheck(this, Coordinate); + + this._x = x; + this._y = y; + } + + _createClass(Coordinate, [{ + key: 'add', + value: function add(coord) { + return new Coordinate(this._x + coord._x, this._y + coord._y); + } + }, { + key: 'sub', + value: function sub(coord) { + return new Coordinate(this._x - coord._x, this._y - coord._y); + } + }, { + key: 'distance', + value: function distance(coord) { + var deltaX = this._x - coord._x; + var deltaY = this._y - coord._y; + + return Math.sqrt(Math.pow(deltaX, 2) + Math.pow(deltaY, 2)); + } + }, { + key: 'max', + value: function max(coord) { + var x = Math.max(this._x, coord._x); + var y = Math.max(this._y, coord._y); + + return new Coordinate(x, y); + } + }, { + key: 'equals', + value: function equals(coord) { + if (this == coord) { + return true; + } + if (!coord || coord == null) { + return false; + } + return this._x == coord._x && this._y == coord._y; + } + }, { + key: 'inside', + value: function inside(northwest, southeast) { + if (this._x >= northwest._x && this._x <= southeast._x && this._y >= northwest._y && this._y <= southeast._y) { + + return true; + } + return false; + } + }, { + key: 'constrain', + value: function constrain(min, max) { + if (min._x > max._x || min._y > max._y) { + return this; + } + + var x = this._x, + y = this._y; + + if (min._x !== null) { + x = Math.max(x, min._x); + } + if (max._x !== null) { + x = Math.min(x, max._x); + } + if (min._y !== null) { + y = Math.max(y, min._y); + } + if (max._y !== null) { + y = Math.min(y, max._y); + } + + return new Coordinate(x, y); + } + }, { + key: 'reposition', + value: function reposition(element) { + element.style['top'] = this._y + 'px'; + element.style['left'] = this._x + 'px'; + } + }, { + key: 'toString', + value: function toString() { + return '(' + this._x + ',' + this._y + ')'; + } + }, { + key: 'x', + get: function get() { + return this._x; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._x = value; + return this; + } + }, { + key: 'y', + get: function get() { + return this._y; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._y = value; + return this; + } + }]); + + return Coordinate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Coordinate); + +/***/ }), +/* 5 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +Object.defineProperty(__webpack_exports__, "__esModule", { value: true }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_index__ = __webpack_require__(6); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_type__ = __webpack_require__(2); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_4__components_autoplay__ = __webpack_require__(7); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__ = __webpack_require__(9); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_6__components_infinite__ = __webpack_require__(10); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_7__components_loop__ = __webpack_require__(11); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_8__components_navigation__ = __webpack_require__(13); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_9__components_pagination__ = __webpack_require__(15); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_10__components_swipe__ = __webpack_require__(18); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_11__components_transitioner__ = __webpack_require__(19); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_12__defaultOptions__ = __webpack_require__(22); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_13__templates__ = __webpack_require__(23); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_14__templates_item__ = __webpack_require__(24); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _defineProperty(obj, key, value) { if (key in obj) { Object.defineProperty(obj, key, { value: value, enumerable: true, configurable: true, writable: true }); } else { obj[key] = value; } return obj; } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + + + + + + + + + + + + + + + + +var bulmaCarousel = function (_EventEmitter) { + _inherits(bulmaCarousel, _EventEmitter); + + function bulmaCarousel(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaCarousel); + + var _this = _possibleConstructorReturn(this, (bulmaCarousel.__proto__ || Object.getPrototypeOf(bulmaCarousel)).call(this)); + + _this.element = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + _this._clickEvents = ['click', 'touch']; + + // Use Element dataset values to override options + var elementConfig = _this.element.dataset ? Object.keys(_this.element.dataset).filter(function (key) { + return Object.keys(__WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */]).includes(key); + }).reduce(function (obj, key) { + return _extends({}, obj, _defineProperty({}, key, _this.element.dataset[key])); + }, {}) : {}; + // Set default options - dataset attributes are master + _this.options = _extends({}, __WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */], options, elementConfig); + + _this._id = Object(__WEBPACK_IMPORTED_MODULE_0__utils_index__["a" /* uuid */])('slider'); + + _this.onShow = _this.onShow.bind(_this); + + // Initiate plugin + _this._init(); + return _this; + } + + /** + * Initiate all DOM element containing datePicker class + * @method + * @return {Array} Array of all datePicker instances + */ + + + _createClass(bulmaCarousel, [{ + key: '_init', + + + /**************************************************** + * * + * PRIVATE FUNCTIONS * + * * + ****************************************************/ + /** + * Initiate plugin instance + * @method _init + * @return {Slider} Current plugin instance + */ + value: function _init() { + this._items = Array.from(this.element.children); + + // Load plugins + this._breakpoint = new __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__["a" /* default */](this); + this._autoplay = new __WEBPACK_IMPORTED_MODULE_4__components_autoplay__["a" /* default */](this); + this._navigation = new __WEBPACK_IMPORTED_MODULE_8__components_navigation__["a" /* default */](this); + this._pagination = new __WEBPACK_IMPORTED_MODULE_9__components_pagination__["a" /* default */](this); + this._infinite = new __WEBPACK_IMPORTED_MODULE_6__components_infinite__["a" /* default */](this); + this._loop = new __WEBPACK_IMPORTED_MODULE_7__components_loop__["a" /* default */](this); + this._swipe = new __WEBPACK_IMPORTED_MODULE_10__components_swipe__["a" /* default */](this); + + this._build(); + + if (Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["a" /* isFunction */])(this.options.onReady)) { + this.options.onReady(this); + } + + return this; + } + + /** + * Build Slider HTML component and append it to the DOM + * @method _build + */ + + }, { + key: '_build', + value: function _build() { + var _this2 = this; + + // Generate HTML Fragment of template + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_13__templates__["a" /* default */])(this.id)); + // Save pointers to template parts + this._ui = { + wrapper: this.node.firstChild, + container: this.node.querySelector('.slider-container') + + // Add slider to DOM + };this.element.appendChild(this.node); + this._ui.wrapper.classList.add('is-loading'); + this._ui.container.style.opacity = 0; + + this._transitioner = new __WEBPACK_IMPORTED_MODULE_11__components_transitioner__["a" /* default */](this); + + // Wrap all items by slide element + this._slides = this._items.map(function (item, index) { + return _this2._createSlide(item, index); + }); + + this.reset(); + + this._bindEvents(); + + this._ui.container.style.opacity = 1; + this._ui.wrapper.classList.remove('is-loading'); + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.on('show', this.onShow); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.off('show', this.onShow); + } + }, { + key: '_createSlide', + value: function _createSlide(item, index) { + var slide = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_14__templates_item__["a" /* default */])()).firstChild; + slide.dataset.sliderIndex = index; + slide.appendChild(item); + return slide; + } + + /** + * Calculate slider dimensions + */ + + }, { + key: '_setDimensions', + value: function _setDimensions() { + var _this3 = this; + + if (!this.options.vertical) { + if (this.options.centerMode) { + this._ui.wrapper.style.padding = '0px ' + this.options.centerPadding; + } + } else { + this._ui.wrapper.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this.slidesToShow; + if (this.options.centerMode) { + this._ui.wrapper.style.padding = this.options.centerPadding + ' 0px'; + } + } + + this._wrapperWidth = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this._ui.wrapper); + this._wrapperHeight = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._ui.wrapper); + + if (!this.options.vertical) { + this._slideWidth = Math.ceil(this._wrapperWidth / this.slidesToShow); + this._containerWidth = Math.ceil(this._slideWidth * this._slides.length); + this._ui.container.style.width = this._containerWidth + 'px'; + } else { + this._slideWidth = Math.ceil(this._wrapperWidth); + this._containerHeight = Math.ceil(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this._slides.length); + this._ui.container.style.height = this._containerHeight + 'px'; + } + + this._slides.forEach(function (slide) { + slide.style.width = _this3._slideWidth + 'px'; + }); + } + }, { + key: '_setHeight', + value: function _setHeight() { + if (this.options.effect !== 'translate') { + this._ui.container.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[this.state.index]) + 'px'; + } + } + + // Update slides classes + + }, { + key: '_setClasses', + value: function _setClasses() { + var _this4 = this; + + this._slides.forEach(function (slide) { + Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["d" /* removeClasses */])(slide, 'is-active is-current is-slide-previous is-slide-next'); + if (Math.abs((_this4.state.index - 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-previous'); + } + if (Math.abs(_this4.state.index % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-current'); + } + if (Math.abs((_this4.state.index + 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-next'); + } + }); + } + + /**************************************************** + * * + * GETTERS and SETTERS * + * * + ****************************************************/ + + /** + * Get id of current datePicker + */ + + }, { + key: 'onShow', + + + /**************************************************** + * * + * EVENTS FUNCTIONS * + * * + ****************************************************/ + value: function onShow(e) { + this._navigation.refresh(); + this._pagination.refresh(); + this._setClasses(); + } + + /**************************************************** + * * + * PUBLIC FUNCTIONS * + * * + ****************************************************/ + + }, { + key: 'next', + value: function next() { + if (!this.options.loop && !this.options.infinite && this.state.index + this.slidesToScroll > this.state.length - this.slidesToShow && !this.options.centerMode) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index + this.slidesToScroll; + } + this.show(); + } + }, { + key: 'previous', + value: function previous() { + if (!this.options.loop && !this.options.infinite && this.state.index === 0) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index - this.slidesToScroll; + } + this.show(); + } + }, { + key: 'start', + value: function start() { + this._autoplay.start(); + } + }, { + key: 'pause', + value: function pause() { + this._autoplay.pause(); + } + }, { + key: 'stop', + value: function stop() { + this._autoplay.stop(); + } + }, { + key: 'show', + value: function show(index) { + var force = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + // If all slides are already visible then return + if (!this.state.length || this.state.length <= this.slidesToShow) { + return; + } + + if (typeof index === 'Number') { + this.state.next = index; + } + + if (this.options.loop) { + this._loop.apply(); + } + if (this.options.infinite) { + this._infinite.apply(); + } + + // If new slide is already the current one then return + if (this.state.index === this.state.next) { + return; + } + + this.emit('before:show', this.state); + this._transitioner.apply(force, this._setHeight.bind(this)); + this.emit('after:show', this.state); + + this.emit('show', this); + } + }, { + key: 'reset', + value: function reset() { + var _this5 = this; + + this.state = { + length: this._items.length, + index: Math.abs(this.options.initialSlide), + next: Math.abs(this.options.initialSlide), + prev: undefined + }; + + // Fix options + if (this.options.loop && this.options.infinite) { + this.options.loop = false; + } + if (this.options.slidesToScroll > this.options.slidesToShow) { + this.options.slidesToScroll = this.slidesToShow; + } + this._breakpoint.init(); + + if (this.state.index >= this.state.length && this.state.index !== 0) { + this.state.index = this.state.index - this.slidesToScroll; + } + if (this.state.length <= this.slidesToShow) { + this.state.index = 0; + } + + this._ui.wrapper.appendChild(this._navigation.init().render()); + this._ui.wrapper.appendChild(this._pagination.init().render()); + + if (this.options.navigationSwipe) { + this._swipe.bindEvents(); + } else { + this._swipe._bindEvents(); + } + + this._breakpoint.apply(); + // Move all created slides into slider + this._slides.forEach(function (slide) { + return _this5._ui.container.appendChild(slide); + }); + this._transitioner.init().apply(true, this._setHeight.bind(this)); + + if (this.options.autoplay) { + this._autoplay.init().start(); + } + } + + /** + * Destroy Slider + * @method destroy + */ + + }, { + key: 'destroy', + value: function destroy() { + var _this6 = this; + + this._unbindEvents(); + this._items.forEach(function (item) { + _this6.element.appendChild(item); + }); + this.node.remove(); + } + }, { + key: 'id', + get: function get() { + return this._id; + } + }, { + key: 'index', + set: function set(index) { + this._index = index; + }, + get: function get() { + return this._index; + } + }, { + key: 'length', + set: function set(length) { + this._length = length; + }, + get: function get() { + return this._length; + } + }, { + key: 'slides', + get: function get() { + return this._slides; + }, + set: function set(slides) { + this._slides = slides; + } + }, { + key: 'slidesToScroll', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToScroll() : 1; + } + }, { + key: 'slidesToShow', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToShow() : 1; + } + }, { + key: 'direction', + get: function get() { + return this.element.dir.toLowerCase() === 'rtl' || this.element.style.direction === 'rtl' ? 'rtl' : 'ltr'; + } + }, { + key: 'wrapper', + get: function get() { + return this._ui.wrapper; + } + }, { + key: 'wrapperWidth', + get: function get() { + return this._wrapperWidth || 0; + } + }, { + key: 'container', + get: function get() { + return this._ui.container; + } + }, { + key: 'containerWidth', + get: function get() { + return this._containerWidth || 0; + } + }, { + key: 'slideWidth', + get: function get() { + return this._slideWidth || 0; + } + }, { + key: 'transitioner', + get: function get() { + return this._transitioner; + } + }], [{ + key: 'attach', + value: function attach() { + var _this7 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : '.slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + [].forEach.call(elements, function (element) { + if (typeof element[_this7.constructor.name] === 'undefined') { + var instance = new bulmaCarousel(element, options); + element[_this7.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this7.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaCarousel; +}(__WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaCarousel); + +/***/ }), +/* 6 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return uuid; }); +/* unused harmony export isRtl */ +/* unused harmony export defer */ +/* unused harmony export getNodeIndex */ +/* unused harmony export camelize */ +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +var uuid = function uuid() { + var prefix = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ''; + return prefix + ([1e7] + -1e3 + -4e3 + -8e3 + -1e11).replace(/[018]/g, function (c) { + return (c ^ crypto.getRandomValues(new Uint8Array(1))[0] & 15 >> c / 4).toString(16); + }); +}; +var isRtl = function isRtl() { + return document.documentElement.getAttribute('dir') === 'rtl'; +}; + +var defer = function defer() { + this.promise = new Promise(function (resolve, reject) { + this.resolve = resolve; + this.reject = reject; + }.bind(this)); + + this.then = this.promise.then.bind(this.promise); + this.catch = this.promise.catch.bind(this.promise); +}; + +var getNodeIndex = function getNodeIndex(node) { + return [].concat(_toConsumableArray(node.parentNode.children)).indexOf(node); +}; +var camelize = function camelize(str) { + return str.replace(/-(\w)/g, toUpper); +}; + +/***/ }), +/* 7 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_device__ = __webpack_require__(8); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + +var onVisibilityChange = Symbol('onVisibilityChange'); +var onMouseEnter = Symbol('onMouseEnter'); +var onMouseLeave = Symbol('onMouseLeave'); + +var defaultOptions = { + autoplay: false, + autoplaySpeed: 3000 +}; + +var Autoplay = function (_EventEmitter) { + _inherits(Autoplay, _EventEmitter); + + function Autoplay(slider) { + _classCallCheck(this, Autoplay); + + var _this = _possibleConstructorReturn(this, (Autoplay.__proto__ || Object.getPrototypeOf(Autoplay)).call(this)); + + _this.slider = slider; + + _this.onVisibilityChange = _this.onVisibilityChange.bind(_this); + _this.onMouseEnter = _this.onMouseEnter.bind(_this); + _this.onMouseLeave = _this.onMouseLeave.bind(_this); + return _this; + } + + _createClass(Autoplay, [{ + key: 'init', + value: function init() { + this._bindEvents(); + return this; + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + document.addEventListener('visibilitychange', this.onVisibilityChange); + if (this.slider.options.pauseOnHover) { + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + document.removeEventListener('visibilitychange', this.onVisibilityChange); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + }, { + key: 'start', + value: function start() { + var _this2 = this; + + this.stop(); + if (this.slider.options.autoplay) { + this.emit('start', this); + this._interval = setInterval(function () { + if (!(_this2._hovering && _this2.slider.options.pauseOnHover)) { + if (!_this2.slider.options.centerMode && _this2.slider.state.next >= _this2.slider.state.length - _this2.slider.slidesToShow && !_this2.slider.options.loop && !_this2.slider.options.infinite) { + _this2.stop(); + } else { + _this2.slider.next(); + } + } + }, this.slider.options.autoplaySpeed); + } + } + }, { + key: 'stop', + value: function stop() { + this._interval = clearInterval(this._interval); + this.emit('stop', this); + } + }, { + key: 'pause', + value: function pause() { + var _this3 = this; + + var speed = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + if (this.paused) { + return; + } + if (this.timer) { + this.stop(); + } + this.paused = true; + if (speed === 0) { + this.paused = false; + this.start(); + } else { + this.slider.on('transition:end', function () { + if (!_this3) { + return; + } + _this3.paused = false; + if (!_this3.run) { + _this3.stop(); + } else { + _this3.start(); + } + }); + } + } + }, { + key: 'onVisibilityChange', + value: function onVisibilityChange(e) { + if (document.hidden) { + this.stop(); + } else { + this.start(); + } + } + }, { + key: 'onMouseEnter', + value: function onMouseEnter(e) { + this._hovering = true; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }, { + key: 'onMouseLeave', + value: function onMouseLeave(e) { + this._hovering = false; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }]); + + return Autoplay; +}(__WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["a"] = (Autoplay); + +/***/ }), +/* 8 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export isIE */ +/* unused harmony export isIETouch */ +/* unused harmony export isAndroid */ +/* unused harmony export isiPad */ +/* unused harmony export isiPod */ +/* unused harmony export isiPhone */ +/* unused harmony export isSafari */ +/* unused harmony export isUiWebView */ +/* unused harmony export supportsTouchEvents */ +/* unused harmony export supportsPointerEvents */ +/* unused harmony export supportsTouch */ +/* unused harmony export pointerDown */ +/* unused harmony export pointerMove */ +/* unused harmony export pointerUp */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return pointerEnter; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return pointerLeave; }); +var isIE = window.navigator.pointerEnabled || window.navigator.msPointerEnabled; +var isIETouch = window.navigator.msPointerEnabled && window.navigator.msMaxTouchPoints > 1 || window.navigator.pointerEnabled && window.navigator.maxTouchPoints > 1; +var isAndroid = navigator.userAgent.match(/(Android);?[\s\/]+([\d.]+)?/); +var isiPad = navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/); +var isiPod = navigator.userAgent.match(/(iPod)(.*OS\s([\d_]+))?/); +var isiPhone = !navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/) && navigator.userAgent.match(/(iPhone\sOS)\s([\d_]+)/); +var isSafari = navigator.userAgent.toLowerCase().indexOf('safari') >= 0 && navigator.userAgent.toLowerCase().indexOf('chrome') < 0 && navigator.userAgent.toLowerCase().indexOf('android') < 0; +var isUiWebView = /(iPhone|iPod|iPad).*AppleWebKit(?!.*Safari)/i.test(navigator.userAgent); + +var supportsTouchEvents = !!('ontouchstart' in window); +var supportsPointerEvents = !!('PointerEvent' in window); +var supportsTouch = supportsTouchEvents || window.DocumentTouch && document instanceof DocumentTouch || navigator.maxTouchPoints; // IE >=11 +var pointerDown = !supportsTouch ? 'mousedown' : 'mousedown ' + (supportsTouchEvents ? 'touchstart' : 'pointerdown'); +var pointerMove = !supportsTouch ? 'mousemove' : 'mousemove ' + (supportsTouchEvents ? 'touchmove' : 'pointermove'); +var pointerUp = !supportsTouch ? 'mouseup' : 'mouseup ' + (supportsTouchEvents ? 'touchend' : 'pointerup'); +var pointerEnter = supportsTouch && supportsPointerEvents ? 'pointerenter' : 'mouseenter'; +var pointerLeave = supportsTouch && supportsPointerEvents ? 'pointerleave' : 'mouseleave'; + +/***/ }), +/* 9 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var onResize = Symbol('onResize'); + +var Breakpoints = function () { + function Breakpoints(slider) { + _classCallCheck(this, Breakpoints); + + this.slider = slider; + this.options = slider.options; + + this[onResize] = this[onResize].bind(this); + + this._bindEvents(); + } + + _createClass(Breakpoints, [{ + key: 'init', + value: function init() { + this._defaultBreakpoint = { + slidesToShow: this.options.slidesToShow, + slidesToScroll: this.options.slidesToScroll + }; + this.options.breakpoints.sort(function (a, b) { + return parseInt(a.changePoint, 10) > parseInt(b.changePoint, 10); + }); + this._currentBreakpoint = this._getActiveBreakpoint(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + window.addEventListener('resize', this[onResize]); + window.addEventListener('orientationchange', this[onResize]); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + window.removeEventListener('resize', this[onResize]); + window.removeEventListener('orientationchange', this[onResize]); + } + }, { + key: '_getActiveBreakpoint', + value: function _getActiveBreakpoint() { + //Get breakpoint for window width + var _iteratorNormalCompletion = true; + var _didIteratorError = false; + var _iteratorError = undefined; + + try { + for (var _iterator = this.options.breakpoints[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { + var point = _step.value; + + if (point.changePoint >= window.innerWidth) { + return point; + } + } + } catch (err) { + _didIteratorError = true; + _iteratorError = err; + } finally { + try { + if (!_iteratorNormalCompletion && _iterator.return) { + _iterator.return(); + } + } finally { + if (_didIteratorError) { + throw _iteratorError; + } + } + } + + return this._defaultBreakpoint; + } + }, { + key: 'getSlidesToShow', + value: function getSlidesToShow() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToShow : this._defaultBreakpoint.slidesToShow; + } + }, { + key: 'getSlidesToScroll', + value: function getSlidesToScroll() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToScroll : this._defaultBreakpoint.slidesToScroll; + } + }, { + key: 'apply', + value: function apply() { + if (this.slider.state.index >= this.slider.state.length && this.slider.state.index !== 0) { + this.slider.state.index = this.slider.state.index - this._currentBreakpoint.slidesToScroll; + } + if (this.slider.state.length <= this._currentBreakpoint.slidesToShow) { + this.slider.state.index = 0; + } + + if (this.options.loop) { + this.slider._loop.init().apply(); + } + + if (this.options.infinite) { + this.slider._infinite.init().apply(); + } + + this.slider._setDimensions(); + this.slider._transitioner.init().apply(true, this.slider._setHeight.bind(this.slider)); + this.slider._setClasses(); + + this.slider._navigation.refresh(); + this.slider._pagination.refresh(); + } + }, { + key: onResize, + value: function value(e) { + var newBreakPoint = this._getActiveBreakpoint(); + if (newBreakPoint.slidesToShow !== this._currentBreakpoint.slidesToShow) { + this._currentBreakpoint = newBreakPoint; + this.apply(); + } + } + }]); + + return Breakpoints; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Breakpoints); + +/***/ }), +/* 10 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Infinite = function () { + function Infinite(slider) { + _classCallCheck(this, Infinite); + + this.slider = slider; + } + + _createClass(Infinite, [{ + key: 'init', + value: function init() { + if (this.slider.options.infinite && this.slider.options.effect === 'translate') { + if (this.slider.options.centerMode) { + this._infiniteCount = Math.ceil(this.slider.slidesToShow + this.slider.slidesToShow / 2); + } else { + this._infiniteCount = this.slider.slidesToShow; + } + + var frontClones = []; + var slideIndex = 0; + for (var i = this.slider.state.length; i > this.slider.state.length - 1 - this._infiniteCount; i -= 1) { + slideIndex = i - 1; + frontClones.unshift(this._cloneSlide(this.slider.slides[slideIndex], slideIndex - this.slider.state.length)); + } + + var backClones = []; + for (var _i = 0; _i < this._infiniteCount + this.slider.state.length; _i += 1) { + backClones.push(this._cloneSlide(this.slider.slides[_i % this.slider.state.length], _i + this.slider.state.length)); + } + + this.slider.slides = [].concat(frontClones, _toConsumableArray(this.slider.slides), backClones); + } + return this; + } + }, { + key: 'apply', + value: function apply() {} + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.slider.options.infinite) { + if (this.slider.state.next >= this.slider.state.length) { + this.slider.state.index = this.slider.state.next = this.slider.state.next - this.slider.state.length; + this.slider.transitioner.apply(true); + } else if (this.slider.state.next < 0) { + this.slider.state.index = this.slider.state.next = this.slider.state.length + this.slider.state.next; + this.slider.transitioner.apply(true); + } + } + } + }, { + key: '_cloneSlide', + value: function _cloneSlide(slide, index) { + var newSlide = slide.cloneNode(true); + newSlide.dataset.sliderIndex = index; + newSlide.dataset.cloned = true; + var ids = newSlide.querySelectorAll('[id]') || []; + ids.forEach(function (id) { + id.setAttribute('id', ''); + }); + return newSlide; + } + }]); + + return Infinite; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Infinite); + +/***/ }), +/* 11 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_dom__ = __webpack_require__(12); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Loop = function () { + function Loop(slider) { + _classCallCheck(this, Loop); + + this.slider = slider; + } + + _createClass(Loop, [{ + key: "init", + value: function init() { + return this; + } + }, { + key: "apply", + value: function apply() { + if (this.slider.options.loop) { + if (this.slider.state.next > 0) { + if (this.slider.state.next < this.slider.state.length) { + if (this.slider.state.next > this.slider.state.length - this.slider.slidesToShow && Object(__WEBPACK_IMPORTED_MODULE_0__utils_dom__["a" /* isInViewport */])(this.slider._slides[this.slider.state.length - 1], this.slider.wrapper)) { + this.slider.state.next = 0; + } else { + this.slider.state.next = Math.min(Math.max(this.slider.state.next, 0), this.slider.state.length - this.slider.slidesToShow); + } + } else { + this.slider.state.next = 0; + } + } else { + if (this.slider.state.next <= 0 - this.slider.slidesToScroll) { + this.slider.state.next = this.slider.state.length - this.slider.slidesToShow; + } else { + this.slider.state.next = 0; + } + } + } + } + }]); + + return Loop; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Loop); + +/***/ }), +/* 12 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isInViewport; }); +var isInViewport = function isInViewport(element, html) { + var rect = element.getBoundingClientRect(); + html = html || document.documentElement; + return rect.top >= 0 && rect.left >= 0 && rect.bottom <= (window.innerHeight || html.clientHeight) && rect.right <= (window.innerWidth || html.clientWidth); +}; + +/***/ }), +/* 13 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_navigation__ = __webpack_require__(14); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Navigation = function () { + function Navigation(slider) { + _classCallCheck(this, Navigation); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPreviousClick = this.onPreviousClick.bind(this); + this.onNextClick = this.onNextClick.bind(this); + this.onKeyUp = this.onKeyUp.bind(this); + } + + _createClass(Navigation, [{ + key: 'init', + value: function init() { + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_navigation__["a" /* default */])(this.slider.options.icons)); + this._ui = { + previous: this.node.querySelector('.slider-navigation-previous'), + next: this.node.querySelector('.slider-navigation-next') + }; + + this._unbindEvents(); + this._bindEvents(); + + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + this.slider.wrapper.addEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this._ui.previous.addEventListener(clickEvent, _this.onPreviousClick); + _this._ui.next.addEventListener(clickEvent, _this.onNextClick); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + this.slider.wrapper.removeEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this2._ui.previous.removeEventListener(clickEvent, _this2.onPreviousClick); + _this2._ui.next.removeEventListener(clickEvent, _this2.onNextClick); + }); + } + }, { + key: 'onNextClick', + value: function onNextClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.next(); + } + } + }, { + key: 'onPreviousClick', + value: function onPreviousClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.previous(); + } + } + }, { + key: 'onKeyUp', + value: function onKeyUp(e) { + if (this.slider.options.keyNavigation) { + if (e.key === 'ArrowRight' || e.key === 'Right') { + this.slider.next(); + } else if (e.key === 'ArrowLeft' || e.key === 'Left') { + this.slider.previous(); + } + } + } + }, { + key: 'refresh', + value: function refresh() { + // let centerOffset = Math.floor(this.options.slidesToShow / 2); + if (!this.slider.options.loop && !this.slider.options.infinite) { + if (this.slider.options.navigation && this.slider.state.length > this.slider.slidesToShow) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + if (this.slider.state.next === 0) { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - this.slider.slidesToShow && !this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - 1 && this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } else { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Navigation; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Navigation); + +/***/ }), +/* 14 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (icons) { + return "

" + icons.previous + "
\n
" + icons.next + "
"; +}); + +/***/ }), +/* 15 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_pagination__ = __webpack_require__(16); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__ = __webpack_require__(17); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + + +var Pagination = function () { + function Pagination(slider) { + _classCallCheck(this, Pagination); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPageClick = this.onPageClick.bind(this); + this.onResize = this.onResize.bind(this); + } + + _createClass(Pagination, [{ + key: 'init', + value: function init() { + this._pages = []; + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_pagination__["a" /* default */])()); + this._ui = { + container: this.node.firstChild + }; + + this._count = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + + this._draw(); + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + window.addEventListener('resize', this.onResize); + window.addEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this._pages.forEach(function (page) { + return page.addEventListener(clickEvent, _this.onPageClick); + }); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + window.removeEventListener('resize', this.onResize); + window.removeEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this2._pages.forEach(function (page) { + return page.removeEventListener(clickEvent, _this2.onPageClick); + }); + }); + } + }, { + key: '_draw', + value: function _draw() { + this._ui.container.innerHTML = ''; + if (this.slider.options.pagination && this.slider.state.length > this.slider.slidesToShow) { + for (var i = 0; i <= this._count; i++) { + var newPageNode = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__["a" /* default */])()).firstChild; + newPageNode.dataset.index = i * this.slider.slidesToScroll; + this._pages.push(newPageNode); + this._ui.container.appendChild(newPageNode); + } + this._bindEvents(); + } + } + }, { + key: 'onPageClick', + value: function onPageClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + this.slider.state.next = e.currentTarget.dataset.index; + this.slider.show(); + } + }, { + key: 'onResize', + value: function onResize() { + this._draw(); + } + }, { + key: 'refresh', + value: function refresh() { + var _this3 = this; + + var newCount = void 0; + + if (this.slider.options.infinite) { + newCount = Math.ceil(this.slider.state.length - 1 / this.slider.slidesToScroll); + } else { + newCount = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + } + if (newCount !== this._count) { + this._count = newCount; + this._draw(); + } + + this._pages.forEach(function (page) { + page.classList.remove('is-active'); + if (parseInt(page.dataset.index, 10) === _this3.slider.state.next % _this3.slider.state.length) { + page.classList.add('is-active'); + } + }); + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Pagination; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Pagination); + +/***/ }), +/* 16 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 17 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 18 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Swipe = function () { + function Swipe(slider) { + _classCallCheck(this, Swipe); + + this.slider = slider; + + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onStartDrag = this.onStartDrag.bind(this); + this.onMoveDrag = this.onMoveDrag.bind(this); + this.onStopDrag = this.onStopDrag.bind(this); + + this._init(); + } + + _createClass(Swipe, [{ + key: '_init', + value: function _init() {} + }, { + key: 'bindEvents', + value: function bindEvents() { + var _this = this; + + this.slider.container.addEventListener('dragstart', function (e) { + if (!_this._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.addEventListener('mousedown', this.onStartDrag); + this.slider.container.addEventListener('touchstart', this.onStartDrag); + + window.addEventListener('mousemove', this.onMoveDrag); + window.addEventListener('touchmove', this.onMoveDrag); + + window.addEventListener('mouseup', this.onStopDrag); + window.addEventListener('touchend', this.onStopDrag); + window.addEventListener('touchcancel', this.onStopDrag); + } + }, { + key: 'unbindEvents', + value: function unbindEvents() { + var _this2 = this; + + this.slider.container.removeEventListener('dragstart', function (e) { + if (!_this2._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.removeEventListener('mousedown', this.onStartDrag); + this.slider.container.removeEventListener('touchstart', this.onStartDrag); + + window.removeEventListener('mousemove', this.onMoveDrag); + window.removeEventListener('touchmove', this.onMoveDrag); + + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('touchcancel', this.onStopDrag); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStartDrag', + value: function onStartDrag(e) { + if (e.touches) { + if (e.touches.length > 1) { + return; + } else { + e = e.touches[0]; + } + } + + this._origin = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](e.screenX, e.screenY); + this.width = this.slider.wrapperWidth; + this.slider.transitioner.disable(); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onMoveDrag', + value: function onMoveDrag(e) { + if (this._origin) { + var point = e.touches ? e.touches[0] : e; + this._lastTranslate = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](point.screenX - this._origin.x, point.screenY - this._origin.y); + if (e.touches) { + if (Math.abs(this._lastTranslate.x) > Math.abs(this._lastTranslate.y)) { + if (!this._supportsPassive) { + e.preventDefault(); + } + e.stopPropagation(); + } + } + } + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStopDrag', + value: function onStopDrag(e) { + if (this._origin && this._lastTranslate) { + if (Math.abs(this._lastTranslate.x) > 0.2 * this.width) { + if (this._lastTranslate.x < 0) { + this.slider.next(); + } else { + this.slider.previous(); + } + } else { + this.slider.show(true); + } + } + this._origin = null; + this._lastTranslate = null; + } + }]); + + return Swipe; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Swipe); + +/***/ }), +/* 19 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__transitions_fade__ = __webpack_require__(20); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__transitions_translate__ = __webpack_require__(21); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Transitioner = function () { + function Transitioner(slider) { + _classCallCheck(this, Transitioner); + + this.slider = slider; + this.options = slider.options; + + this._animating = false; + this._animation = undefined; + + this._translate = new __WEBPACK_IMPORTED_MODULE_1__transitions_translate__["a" /* default */](this, slider, slider.options); + this._fade = new __WEBPACK_IMPORTED_MODULE_0__transitions_fade__["a" /* default */](this, slider, slider.options); + } + + _createClass(Transitioner, [{ + key: 'init', + value: function init() { + this._fade.init(); + this._translate.init(); + return this; + } + }, { + key: 'isAnimating', + value: function isAnimating() { + return this._animating; + } + }, { + key: 'enable', + value: function enable() { + this._animation && this._animation.enable(); + } + }, { + key: 'disable', + value: function disable() { + this._animation && this._animation.disable(); + } + }, { + key: 'apply', + value: function apply(force, callback) { + // If we don't force refresh and animation in progress then return + if (this._animating && !force) { + return; + } + + switch (this.options.effect) { + case 'fade': + this._animation = this._fade; + break; + case 'translate': + default: + this._animation = this._translate; + break; + } + + this._animationCallback = callback; + + if (force) { + this._animation && this._animation.disable(); + } else { + this._animation && this._animation.enable(); + this._animating = true; + } + + this._animation && this._animation.apply(); + + if (force) { + this.end(); + } + } + }, { + key: 'end', + value: function end() { + this._animating = false; + this._animation = undefined; + this.slider.state.index = this.slider.state.next; + if (this._animationCallback) { + this._animationCallback(); + } + } + }]); + + return Transitioner; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Transitioner); + +/***/ }), +/* 20 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Fade = function () { + function Fade(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Fade); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + } + + _createClass(Fade, [{ + key: 'init', + value: function init() { + var _this = this; + + if (this.options.effect === 'fade') { + this.slider.slides.forEach(function (slide, index) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(slide, { + position: 'absolute', + left: 0, + top: 0, + bottom: 0, + 'z-index': slide.dataset.sliderIndex == _this.slider.state.index ? 0 : -2, + opacity: slide.dataset.sliderIndex == _this.slider.state.index ? 1 : 0 + }); + }); + } + return this; + } + }, { + key: 'enable', + value: function enable() { + var _this2 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + if (this._oldSlide) { + this._oldSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + } + } + }, { + key: 'disable', + value: function disable() { + var _this3 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = 'none'; + if (this._oldSlide) { + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = 'none'; + } + } + } + }, { + key: 'apply', + value: function apply(force) { + var _this4 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.next; + })[0]; + + if (this._oldSlide && this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + opacity: 0 + }); + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + opacity: 1, + 'z-index': force ? 0 : -1 + }); + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'fade') { + if (this.transitioner.isAnimating() && e.target == this._newSlide) { + if (this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + 'z-index': 0 + }); + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + if (this._oldSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + 'z-index': -2 + }); + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + } + this.transitioner.end(); + } + } + }]); + + return Fade; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Fade); + +/***/ }), +/* 21 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Translate = function () { + function Translate(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Translate); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + + this.onTransitionEnd = this.onTransitionEnd.bind(this); + } + + _createClass(Translate, [{ + key: 'init', + value: function init() { + this._position = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](this.slider.container.offsetLeft, this.slider.container.offsetTop); + this._bindEvents(); + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.slider.container.addEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.slider.container.removeEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: 'enable', + value: function enable() { + this.slider.container.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + }, { + key: 'disable', + value: function disable() { + this.slider.container.style.transition = 'none'; + } + }, { + key: 'apply', + value: function apply() { + var _this = this; + + var maxOffset = void 0; + if (this.options.effect === 'translate') { + var slide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this.slider.state.next; + })[0]; + var slideOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](slide.offsetLeft, slide.offsetTop); + if (this.options.centerMode) { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container))); + } else { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.wrapper)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.wrapper))); + } + var nextOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.min(Math.max(slideOffset.x * -1, maxOffset.x * -1), 0), Math.min(Math.max(slideOffset.y * -1, maxOffset.y * -1), 0)); + if (this.options.loop) { + if (!this.options.vertical && Math.abs(this._position.x) > maxOffset.x) { + nextOffset.x = 0; + this.slider.state.next = 0; + } else if (this.options.vertical && Math.abs(this._position.y) > maxOffset.y) { + nextOffset.y = 0; + this.slider.state.next = 0; + } + } + + this._position.x = nextOffset.x; + this._position.y = nextOffset.y; + if (this.options.centerMode) { + this._position.x = this._position.x + this.slider.wrapperWidth / 2 - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(slide) / 2; + } + + if (this.slider.direction === 'rtl') { + this._position.x = -this._position.x; + this._position.y = -this._position.y; + } + this.slider.container.style.transform = 'translate3d(' + this._position.x + 'px, ' + this._position.y + 'px, 0)'; + + /** + * update the index with the nextIndex only if + * the offset of the nextIndex is in the range of the maxOffset + */ + if (slideOffset.x > maxOffset.x) { + this.slider.transitioner.end(); + } + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'translate') { + + if (this.transitioner.isAnimating() && e.target == this.slider.container) { + if (this.options.infinite) { + this.slider._infinite.onTransitionEnd(e); + } + } + this.transitioner.end(); + } + } + }]); + + return Translate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Translate); + +/***/ }), +/* 22 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var defaultOptions = { + initialSlide: 0, + slidesToScroll: 1, + slidesToShow: 1, + + navigation: true, + navigationKeys: true, + navigationSwipe: true, + + pagination: true, + + loop: false, + infinite: false, + + effect: 'translate', + duration: 300, + timing: 'ease', + + autoplay: false, + autoplaySpeed: 3000, + pauseOnHover: true, + breakpoints: [{ + changePoint: 480, + slidesToShow: 1, + slidesToScroll: 1 + }, { + changePoint: 640, + slidesToShow: 2, + slidesToScroll: 2 + }, { + changePoint: 768, + slidesToShow: 3, + slidesToScroll: 3 + }], + + onReady: null, + icons: { + 'previous': '\n \n ', + 'next': '\n \n ' + } +}; + +/* harmony default export */ __webpack_exports__["a"] = (defaultOptions); + +/***/ }), +/* 23 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (id) { + return "
\n
\n
"; +}); + +/***/ }), +/* 24 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }) +/******/ ])["default"]; +}); \ No newline at end of file diff --git a/docs/static/js/bulma-carousel.min.js b/docs/static/js/bulma-carousel.min.js new file mode 100644 index 0000000..5fff069 --- /dev/null +++ b/docs/static/js/bulma-carousel.min.js @@ -0,0 +1 @@ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.bulmaCarousel=e():t.bulmaCarousel=e()}("undefined"!=typeof self?self:this,function(){return function(i){var n={};function s(t){if(n[t])return n[t].exports;var e=n[t]={i:t,l:!1,exports:{}};return i[t].call(e.exports,e,e.exports,s),e.l=!0,e.exports}return s.m=i,s.c=n,s.d=function(t,e,i){s.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:i})},s.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return s.d(e,"a",e),e},s.o=function(t,e){return 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'}}]).default}); \ No newline at end of file diff --git a/docs/static/js/bulma-slider.js b/docs/static/js/bulma-slider.js new file mode 100644 index 0000000..c6718de --- /dev/null +++ b/docs/static/js/bulma-slider.js @@ -0,0 +1,461 @@ +(function webpackUniversalModuleDefinition(root, factory) { + if(typeof exports === 'object' && typeof module === 'object') + module.exports = factory(); + else if(typeof define === 'function' && define.amd) + define([], factory); + else if(typeof exports === 'object') + exports["bulmaSlider"] = factory(); + else + root["bulmaSlider"] = factory(); +})(typeof self !== 'undefined' ? self : this, function() { +return /******/ (function(modules) { // webpackBootstrap +/******/ // The module cache +/******/ var installedModules = {}; +/******/ +/******/ // The require function +/******/ function __webpack_require__(moduleId) { +/******/ +/******/ // Check if module is in cache +/******/ if(installedModules[moduleId]) { +/******/ return installedModules[moduleId].exports; +/******/ } +/******/ // Create a new module (and put it into the cache) +/******/ var module = installedModules[moduleId] = { +/******/ i: moduleId, +/******/ l: false, +/******/ exports: {} +/******/ }; +/******/ +/******/ // Execute the module function +/******/ modules[moduleId].call(module.exports, module, module.exports, __webpack_require__); +/******/ +/******/ // Flag the module as loaded +/******/ module.l = true; +/******/ +/******/ // Return the exports of the module +/******/ return module.exports; +/******/ } +/******/ +/******/ +/******/ // expose the modules object (__webpack_modules__) +/******/ __webpack_require__.m = modules; +/******/ +/******/ // expose the module cache +/******/ __webpack_require__.c = installedModules; +/******/ +/******/ // define getter function for harmony exports +/******/ __webpack_require__.d = function(exports, name, getter) { +/******/ if(!__webpack_require__.o(exports, name)) { +/******/ Object.defineProperty(exports, name, { +/******/ configurable: false, +/******/ enumerable: true, +/******/ get: getter +/******/ }); +/******/ } +/******/ }; +/******/ +/******/ // getDefaultExport function for compatibility with non-harmony modules +/******/ __webpack_require__.n = function(module) { +/******/ var getter = module && module.__esModule ? +/******/ function getDefault() { return module['default']; } : +/******/ function getModuleExports() { return module; }; +/******/ __webpack_require__.d(getter, 'a', getter); +/******/ return getter; +/******/ }; +/******/ +/******/ // Object.prototype.hasOwnProperty.call +/******/ __webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); }; +/******/ +/******/ // __webpack_public_path__ +/******/ __webpack_require__.p = ""; +/******/ +/******/ // Load entry module and return exports +/******/ return __webpack_require__(__webpack_require__.s = 0); +/******/ }) +/************************************************************************/ +/******/ ([ +/* 0 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +Object.defineProperty(__webpack_exports__, "__esModule", { value: true }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "isString", function() { return isString; }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__events__ = __webpack_require__(1); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; + +var bulmaSlider = function (_EventEmitter) { + _inherits(bulmaSlider, _EventEmitter); + + function bulmaSlider(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaSlider); + + var _this = _possibleConstructorReturn(this, (bulmaSlider.__proto__ || Object.getPrototypeOf(bulmaSlider)).call(this)); + + _this.element = typeof selector === 'string' ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + + _this._clickEvents = ['click']; + /// Set default options and merge with instance defined + _this.options = _extends({}, options); + + _this.onSliderInput = _this.onSliderInput.bind(_this); + + _this.init(); + return _this; + } + + /** + * Initiate all DOM element containing selector + * @method + * @return {Array} Array of all slider instances + */ + + + _createClass(bulmaSlider, [{ + key: 'init', + + + /** + * Initiate plugin + * @method init + * @return {void} + */ + value: function init() { + this._id = 'bulmaSlider' + new Date().getTime() + Math.floor(Math.random() * Math.floor(9999)); + this.output = this._findOutputForSlider(); + + this._bindEvents(); + + if (this.output) { + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + } + + this.emit('bulmaslider:ready', this.element.value); + } + }, { + key: '_findOutputForSlider', + value: function _findOutputForSlider() { + var _this2 = this; + + var result = null; + var outputs = document.getElementsByTagName('output') || []; + + Array.from(outputs).forEach(function (output) { + if (output.htmlFor == _this2.element.getAttribute('id')) { + result = output; + return true; + } + }); + return result; + } + }, { + key: '_getSliderOutputPosition', + value: function _getSliderOutputPosition() { + // Update output position + var newPlace, minValue; + + var style = window.getComputedStyle(this.element, null); + // Measure width of range input + var sliderWidth = parseInt(style.getPropertyValue('width'), 10); + + // Figure out placement percentage between left and right of input + if (!this.element.getAttribute('min')) { + minValue = 0; + } else { + minValue = this.element.getAttribute('min'); + } + var newPoint = (this.element.value - minValue) / (this.element.getAttribute('max') - minValue); + + // Prevent bubble from going beyond left or right (unsupported browsers) + if (newPoint < 0) { + newPlace = 0; + } else if (newPoint > 1) { + newPlace = sliderWidth; + } else { + newPlace = sliderWidth * newPoint; + } + + return { + 'position': newPlace + 'px' + }; + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + if (this.output) { + // Add event listener to update output when slider value change + this.element.addEventListener('input', this.onSliderInput, false); + } + } + }, { + key: 'onSliderInput', + value: function onSliderInput(e) { + e.preventDefault(); + + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + + // Check for prefix and postfix + var prefix = this.output.hasAttribute('data-prefix') ? this.output.getAttribute('data-prefix') : ''; + var postfix = this.output.hasAttribute('data-postfix') ? this.output.getAttribute('data-postfix') : ''; + + // Update output with slider value + this.output.value = prefix + this.element.value + postfix; + + this.emit('bulmaslider:ready', this.element.value); + } + }], [{ + key: 'attach', + value: function attach() { + var _this3 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'input[type="range"].slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = isString(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + elements.forEach(function (element) { + if (typeof element[_this3.constructor.name] === 'undefined') { + var instance = new bulmaSlider(element, options); + element[_this3.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this3.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaSlider; +}(__WEBPACK_IMPORTED_MODULE_0__events__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaSlider); + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var listeners = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this._listeners = new Map(listeners); + this._middlewares = new Map(); + } + + _createClass(EventEmitter, [{ + key: "listenerCount", + value: function listenerCount(eventName) { + if (!this._listeners.has(eventName)) { + return 0; + } + + var eventListeners = this._listeners.get(eventName); + return eventListeners.length; + } + }, { + key: "removeListeners", + value: function removeListeners() { + var _this = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + var middleware = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this.removeListeners(e, middleware); + }); + } else { + this._listeners.delete(eventName); + + if (middleware) { + this.removeMiddleware(eventName); + } + } + } else { + this._listeners = new Map(); + } + } + }, { + key: "middleware", + value: function middleware(eventName, fn) { + var _this2 = this; + + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this2.middleware(e, fn); + }); + } else { + if (!Array.isArray(this._middlewares.get(eventName))) { + this._middlewares.set(eventName, []); + } + + this._middlewares.get(eventName).push(fn); + } + } + }, { + key: "removeMiddleware", + value: function removeMiddleware() { + var _this3 = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this3.removeMiddleware(e); + }); + } else { + this._middlewares.delete(eventName); + } + } else { + this._middlewares = new Map(); + } + } + }, { + key: "on", + value: function on(name, callback) { + var _this4 = this; + + var once = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + if (Array.isArray(name)) { + name.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + name = name.toString(); + var split = name.split(/,|, | /); + + if (split.length > 1) { + split.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + if (!Array.isArray(this._listeners.get(name))) { + this._listeners.set(name, []); + } + + this._listeners.get(name).push({ once: once, callback: callback }); + } + } + } + }, { + key: "once", + value: function once(name, callback) { + this.on(name, callback, true); + } + }, { + key: "emit", + value: function emit(name, data) { + var _this5 = this; + + var silent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + name = name.toString(); + var listeners = this._listeners.get(name); + var middlewares = null; + var doneCount = 0; + var execute = silent; + + if (Array.isArray(listeners)) { + listeners.forEach(function (listener, index) { + // Start Middleware checks unless we're doing a silent emit + if (!silent) { + middlewares = _this5._middlewares.get(name); + // Check and execute Middleware + if (Array.isArray(middlewares)) { + middlewares.forEach(function (middleware) { + middleware(data, function () { + var newData = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + + if (newData !== null) { + data = newData; + } + doneCount++; + }, name); + }); + + if (doneCount >= middlewares.length) { + execute = true; + } + } else { + execute = true; + } + } + + // If Middleware checks have been passed, execute + if (execute) { + if (listener.once) { + listeners[index] = null; + } + listener.callback(data); + } + }); + + // Dirty way of removing used Events + while (listeners.indexOf(null) !== -1) { + listeners.splice(listeners.indexOf(null), 1); + } + } + } + }]); + + return EventEmitter; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (EventEmitter); + +/***/ }) +/******/ ])["default"]; +}); \ No newline at end of file diff --git a/docs/static/js/bulma-slider.min.js b/docs/static/js/bulma-slider.min.js new file mode 100644 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(element && element.bulmaCarousel) { + // bulmaCarousel instance is available as element.bulmaCarousel + element.bulmaCarousel.on('before-show', function(state) { + console.log(state); + }); + } + + /*var player = document.getElementById('interpolation-video'); + player.addEventListener('loadedmetadata', function() { + $('#interpolation-slider').on('input', function(event) { + console.log(this.value, player.duration); + player.currentTime = player.duration / 100 * this.value; + }) + }, false);*/ + preloadInterpolationImages(); + + $('#interpolation-slider').on('input', function(event) { + setInterpolationImage(this.value); + }); + setInterpolationImage(0); + $('#interpolation-slider').prop('max', NUM_INTERP_FRAMES - 1); + + bulmaSlider.attach(); + +}) diff --git a/eval.py b/eval.py new file mode 100644 index 0000000..4eec8dd --- /dev/null +++ b/eval.py @@ -0,0 +1,107 @@ +import argparse + +import dgl +from langchain_text_splitters import TokenTextSplitter + +from retrieval import * +from utils import * +from prompt_pool import * +from data_process import get_processed_data, split_corpus_by_doc, eval_data_generation +from graph_construction import mem_retrieval +from training_preparation import integrate_isolated +from train import GoR + + +def infer_node_embedding(dgl_graph, model_path): + model = GoR(in_dim=IN_DIM, num_hidden=HIDDEN_DIM, num_layer=NUM_LAYER, n_head=N_HEAD) + model.load_state_dict(torch.load(model_path)) + model = model.encoder + model.eval() + model.to(DEVICE) + dgl_graph = dgl_graph.to(DEVICE) + dgl_graph = dgl.add_self_loop(dgl_graph) + node_embedding = model(dgl_graph, dgl_graph.ndata['feat']).detach() + node_embedding = [i for i in node_embedding] + + return node_embedding + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--dataset", type=str, required=True) + parser.add_argument("--llm_model", type=str, default="meta-llama/Llama-2-7b-chat-hf") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--cuda", type=int, default=0) + parser.add_argument("--tau", type=float, default=0) + parser.add_argument("--retriever", type=str, default="contriever") + parser.add_argument("--chunk_size", type=int, default=256) + parser.add_argument("--chunk_overlap", type=int, default=32) + parser.add_argument("--recall_chunk_num", type=int, default=6) + parser.add_argument("--in_dim", type=int, default=768) + parser.add_argument("--hidden_dim", type=int, default=768) + parser.add_argument("--num_layer", type=int, default=2) + parser.add_argument("--n_head", type=int, default=4) + opt = parser.parse_args() + DATASET = opt.dataset + LLM_MODEL = opt.llm_model + SEED = opt.seed + TAU = opt.tau + RETRIEVER = opt.retriever + CHUNK_SIZE = opt.chunk_size + CHUNK_OVERLAP = opt.chunk_overlap + RECALL_CHUNK_NUM = opt.recall_chunk_num + IN_DIM = opt.in_dim + HIDDEN_DIM = opt.hidden_dim + NUM_LAYER = opt.num_layer + N_HEAD = opt.n_head + + set_seed(int(SEED)) + DEVICE = get_device(int(opt.cuda)) + + QUERY_TOKENIZER, CTX_TOKENIZER, QUERY_ENCODER, CTX_ENCODER = get_dense_retriever(retriever=RETRIEVER) + QUERY_ENCODER = QUERY_ENCODER.to(DEVICE) + CTX_ENCODER = CTX_ENCODER.to(DEVICE) + + TEXT_SPLITTER = TokenTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP) + + data = get_processed_data(dataset=DATASET, train=False) + print("{} #Data: {}".format(show_time(), len(data))) + data = data[:30] + check_path("./graph") + check_path("./result") + result_recorder = dict() + for ind, sample in enumerate(data): + all_doc_chunk_list = split_corpus_by_doc(dataset=DATASET, sample=sample, text_splitter=TEXT_SPLITTER) + all_doc_chunk_list_embedding = get_dense_embedding(all_doc_chunk_list, retriever=RETRIEVER, + tokenizer=CTX_TOKENIZER, + model=CTX_ENCODER) + graph = load_nx(path="./graph/{}_test_graph_{}.graphml".format(DATASET, ind)) + gs, _ = dgl.load_graphs("./graph/{}_test_graph_{}.dgl".format(DATASET, ind)) + dgl_graph = gs[0] + graph, dgl_graph, = integrate_isolated(graph=graph, dgl_graph=dgl_graph, all_doc_chunk_list=all_doc_chunk_list, + all_doc_chunk_list_embedding=all_doc_chunk_list_embedding) + check_path("./weights") + mem_chunk_embedding = infer_node_embedding(dgl_graph=dgl_graph, model_path="./weights/{}.pth".format(DATASET)) + eval_data = eval_data_generation(dataset=DATASET, sample=sample) + for test_query in eval_data: + retrieved_chunks, _ = mem_retrieval(mem_chunk_embedding=mem_chunk_embedding, + rag_query=test_query["rag_query"], + graph=graph, + all_doc_chunk_list=all_doc_chunk_list, + all_doc_chunk_list_embedding=all_doc_chunk_list_embedding, + retriever=RETRIEVER, + query_tokenizer=QUERY_TOKENIZER, + query_encoder=QUERY_ENCODER, + recall_chunk_num=RECALL_CHUNK_NUM) + response = get_llm_response_via_api( + prompt=QUERY_PROMPT_NORMAL[DATASET].format_map({"question": test_query["query"], + "materials": "\n\n".join( + retrieved_chunks)}), + LLM_MODEL=LLM_MODEL, + TAU=TAU, + SEED=SEED) + # print(text_wrap("LLM 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b/graph_construction.py @@ -0,0 +1,216 @@ +import argparse + +import dgl +from langchain_text_splitters import TokenTextSplitter + +from retrieval import * +from utils import * +from prompt_pool import * +from data_process import get_processed_data, split_corpus_by_doc + + +def rag_retrieval(chunk_list, rag_query, chunk_embedding=None): + if len(chunk_list) <= RECALL_CHUNK_NUM: + return chunk_list + if chunk_embedding is None: + chunk_embedding = get_dense_embedding(chunk_list, retriever=RETRIEVER, tokenizer=CTX_TOKENIZER, + model=CTX_ENCODER) + rag_query_embedding = get_dense_embedding([rag_query], retriever=RETRIEVER, tokenizer=QUERY_TOKENIZER, + model=QUERY_ENCODER) + assert len(rag_query_embedding) == 1 + _, retrieved_text_list = run_dense_retrieval(rag_query_embedding, chunk_embedding, chunk_list, + chunk_num=RECALL_CHUNK_NUM) + + return retrieved_text_list + + +def mem_retrieval(mem_chunk_embedding, all_doc_chunk_list, all_doc_chunk_list_embedding, rag_query, graph, retriever, + query_tokenizer, query_encoder, recall_chunk_num): + mem_chunk_list = [] + for node, attrs in graph.nodes(data=True): + mem_chunk_list.append(node) + assert len(mem_chunk_embedding) == len(mem_chunk_list), "{}!={}".format(len(mem_chunk_embedding), + len(mem_chunk_list)) + mem_chunk_embedding_copy = [i for i in mem_chunk_embedding] + for chunk, chunk_embedding in zip(all_doc_chunk_list, all_doc_chunk_list_embedding): + if chunk not in mem_chunk_list: + mem_chunk_list.append(chunk) + mem_chunk_embedding_copy.append(chunk_embedding) + rag_query_embedding = get_dense_embedding([rag_query], retriever=retriever, tokenizer=query_tokenizer, + model=query_encoder) + mem_chunk_embedding_copy = [i.to(rag_query_embedding[0].device) for i in mem_chunk_embedding_copy] + assert len(rag_query_embedding) == 1 + assert len(mem_chunk_embedding_copy) == len(mem_chunk_list) + retrieved_index, retrieved_text_list = run_dense_retrieval(rag_query_embedding, mem_chunk_embedding_copy, + mem_chunk_list, chunk_num=recall_chunk_num) + + return retrieved_text_list, retrieved_index + + +def get_node_embedding_list(dgl_graph): + mem_chunk_embedding = dgl_graph.ndata['feat'] + mem_chunk_embedding = [i for i in mem_chunk_embedding] + + return mem_chunk_embedding + + +def record_graph_construction(query, support_materials, response, graph, dgl_graph, training_data, answer=None): + sub_training_data = dict() + sub_training_data["query"] = query + if answer: + sub_training_data["answer"] = answer + existing_chunks = [] + for node, attrs in graph.nodes(data=True): + existing_chunks.append(node) + non_dup_chunks = [] + if response not in existing_chunks: + non_dup_chunks.append(response) + graph.add_node( + response, + ) + existing_chunks.append(response) + for chunk in support_materials: + if chunk not in existing_chunks: + non_dup_chunks.append(chunk) + graph.add_node( + chunk, + ) + existing_chunks.append(chunk) + chunk_id_map = dict() + for chunk_id, chunk in enumerate(existing_chunks): + chunk_id_map[chunk] = chunk_id + if len(non_dup_chunks) != 0: + new_node_embedding = get_dense_embedding(non_dup_chunks, retriever=RETRIEVER, tokenizer=CTX_TOKENIZER, + model=CTX_ENCODER) + dgl_graph.add_nodes(num=len(non_dup_chunks), data={'feat': torch.vstack(new_node_embedding).cpu()}) + sub_training_data["response"] = [chunk_id_map[response]] + sub_training_data["raw"] = [] + for chunk in support_materials: + sub_training_data["raw"].append(chunk_id_map[chunk]) + if not graph.has_edge(chunk, response): + graph.add_edge( + chunk, + response, + weight=1 + ) + if not dgl_graph.has_edges_between(chunk_id_map[chunk], chunk_id_map[response]): + dgl_graph.add_edges(chunk_id_map[chunk], + chunk_id_map[response], + data={'w': torch.ones(1, 1)}) + + training_data.append(sub_training_data) + + return graph, dgl_graph, training_data + + +def llm2query(prompt, tau=0.5): + content = get_llm_response_via_api(prompt=prompt, + LLM_MODEL=LLM_MODEL, + TAU=tau, + SEED=SEED) + content = content.split("\n") + for ind, c in enumerate(content): + for start_ind in range(len(c)): + if str(c[start_ind]).isalpha(): + break + content[ind] = c[start_ind:] + + return [i for i in content if len(i.strip()) != 0] + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--dataset", type=str, required=True) + parser.add_argument('--train', action='store_true') + parser.add_argument("--llm_model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--cuda", type=int, default=0) + parser.add_argument("--tau", type=float, default=0) + parser.add_argument("--query_tau", type=float, default=0.5) + parser.add_argument("--retriever", type=str, default="contriever") + parser.add_argument("--chunk_size", type=int, default=256) + parser.add_argument("--chunk_overlap", type=int, default=32) + parser.add_argument("--recall_chunk_num", type=int, default=6) + parser.add_argument("--query_num", type=int, default=30) + opt = parser.parse_args() + DATASET = opt.dataset + TRAIN = opt.train + LLM_MODEL = opt.llm_model + SEED = opt.seed + TAU = opt.tau + QUERY_TAU = opt.query_tau + RETRIEVER = opt.retriever + CHUNK_SIZE = opt.chunk_size + CHUNK_OVERLAP = opt.chunk_overlap + RECALL_CHUNK_NUM = opt.recall_chunk_num + QUERY_NUM = opt.query_num + + set_seed(int(SEED)) + DEVICE = get_device(int(opt.cuda)) + + QUERY_TOKENIZER, CTX_TOKENIZER, QUERY_ENCODER, CTX_ENCODER = get_dense_retriever(retriever=RETRIEVER) + QUERY_ENCODER = QUERY_ENCODER.to(DEVICE) + CTX_ENCODER = CTX_ENCODER.to(DEVICE) + + TEXT_SPLITTER = TokenTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP) + + data = get_processed_data(dataset=DATASET, train=TRAIN) + print("{} #Data: {}".format(show_time(), len(data))) + MAX_NUM = 400 if TRAIN else 30 + data = data[:MAX_NUM] + check_path("./graph") + for ind, sample in enumerate(data): + # Due to budget constraints, we randomly select at most 400 samples for training and 30 samples for evaluation. + # You can optionally create a dev set for hyper-parameter tuning + all_doc_chunk_list = split_corpus_by_doc(dataset=DATASET, sample=sample, text_splitter=TEXT_SPLITTER) + all_doc_chunk_list_embedding = get_dense_embedding(all_doc_chunk_list, retriever=RETRIEVER, + tokenizer=CTX_TOKENIZER, + model=CTX_ENCODER) + graph = nx.Graph() + dgl_graph = dgl.graph(([], []), num_nodes=0) + training_data = [] + # Query Simulation + user_question = [] + user_answer = [] + while len(user_question) < QUERY_NUM: + unsup_answer = np.random.choice(all_doc_chunk_list, size=1, replace=False)[0].split() + unsup_answer = " ".join(unsup_answer) + gen_q = llm2query(prompt=QUERY_GENERATE.format_map({"document": unsup_answer}), tau=QUERY_TAU)[0] + if gen_q not in user_question: + user_question.append(gen_q) + user_answer.append(unsup_answer) + print("{} Generate Query {}/{}:\n{}".format(show_time(), len(user_question), QUERY_NUM, gen_q)) + # Graph Construction + for uid, user_query in enumerate(user_question): + if graph.number_of_nodes() == 0: + retrieved_chunks = rag_retrieval(chunk_list=all_doc_chunk_list, rag_query=user_query, + chunk_embedding=all_doc_chunk_list_embedding) + else: + mem_chunk_embedding = get_node_embedding_list(dgl_graph=dgl_graph) + retrieved_chunks, _ = mem_retrieval(mem_chunk_embedding=mem_chunk_embedding, rag_query=user_query, + graph=graph, all_doc_chunk_list=all_doc_chunk_list, + all_doc_chunk_list_embedding=all_doc_chunk_list_embedding, + retriever=RETRIEVER, query_tokenizer=QUERY_TOKENIZER, + query_encoder=QUERY_ENCODER, recall_chunk_num=RECALL_CHUNK_NUM) + response = get_llm_response_via_api(prompt=QUERY_PROMPT[DATASET].format_map({"question": user_query, + "materials": "\n\n".join( + retrieved_chunks)}), + LLM_MODEL=LLM_MODEL, + TAU=TAU, + SEED=SEED) + graph, dgl_graph, training_data = record_graph_construction(query=user_query, + support_materials=retrieved_chunks, + response=response, graph=graph, + dgl_graph=dgl_graph, + training_data=training_data, + answer=user_answer[uid]) + print("{} Graph Construction: {}/{}".format(show_time(), uid + 1, len(user_question))) + print(dgl_graph) + # Save + if TRAIN: + store_nx(nx_obj=graph, path="./graph/{}_graph_{}.graphml".format(DATASET, ind)) + dgl.save_graphs(filename="./graph/{}_graph_{}.dgl".format(DATASET, ind), g_list=[dgl_graph]) + write_to_pkl(data=training_data, output_file="./graph/{}_training_data_{}.pkl".format(DATASET, ind)) + else: + store_nx(nx_obj=graph, path="./graph/{}_test_graph_{}.graphml".format(DATASET, ind)) + dgl.save_graphs(filename="./graph/{}_test_graph_{}.dgl".format(DATASET, ind), g_list=[dgl_graph]) diff --git a/index.html b/index.html deleted file mode 100644 index 6e7c1e6..0000000 --- a/index.html +++ /dev/null @@ -1 +0,0 @@ -

hello

diff --git a/prompt_pool.py b/prompt_pool.py new file mode 100644 index 0000000..7ab360d --- /dev/null +++ b/prompt_pool.py @@ -0,0 +1,143 @@ +QUERY_GENERATE = """ +You are a great questioner of any text, and are adept at asking valuable and insightful questions. +Your goal is to generate 1 summary question for the text provided below. +The generated summary question should try to simulate the tone of human questions as much as possible, +and make sure that the generated question must be interrogative sentences and a summary question. +Important! Please make sure this text must be a complete and non-redundant answer to the generated summary question. +Please directly output the generated summary question, do not output irrelevant text. + +DOCUMENT: +{document} +""" + + +QUERY_PROMPT_QMSUM = """ +Refer to the following meeting transcripts and answer the question with brief but complete explanations. + +SUPPORTING MATERIALS: +{materials} + +QUESTION: +{question} +""" + + +QUERY_PROMPT_QMSUM_NORMAL = """ +Refer to the following meeting transcripts and answer the question. + +SUPPORTING MATERIALS: +{materials} + +QUESTION: +{question} +""" + + +QUERY_PROMPT_SQuALITY = """ +Refer to the following story and answer the question with brief but complete explanations. + +SUPPORTING MATERIALS: +{materials} + +QUESTION: +{question} +""" + + +QUERY_PROMPT_SQuALITY_NORMAL = """ +Refer to the following story and answer the question. + +SUPPORTING MATERIALS: +{materials} + +QUESTION: +{question} +""" + + +QUERY_PROMPT_GOV = """ +Refer to the following report and answer the question with brief but complete explanations. + +SUPPORTING MATERIALS: +{materials} + +QUESTION: +{question} +""" + + +QUERY_PROMPT_GOV_NORMAL = """ +Refer to the following report and answer the question. + +SUPPORTING MATERIALS: +{materials} + +QUESTION: +{question} +""" + + +QUERY_PROMPT_WCEP = """ +Refer to the following document and answer the question with brief but complete explanations. + +SUPPORTING MATERIALS: +{materials} + +QUESTION: +{question} +""" + + +QUERY_PROMPT_WCEP_NORMAL = """ +Refer to the following document and answer the question. + +SUPPORTING MATERIALS: +{materials} + +QUESTION: +{question} +""" + + +QUERY_PROMPT_BOOK = """ +Refer to the following narrative and answer the question with brief but complete explanations. + +SUPPORTING MATERIALS: +{materials} + +QUESTION: +{question} +""" + + +QUERY_PROMPT_BOOK_NORMAL = """ +Refer to the following narrative and answer the question. + +SUPPORTING MATERIALS: +{materials} + +QUESTION: +{question} +""" + + +QUERY_PROMPT = { + "qmsum": QUERY_PROMPT_QMSUM, + "wcep": QUERY_PROMPT_WCEP, + "booksum": QUERY_PROMPT_BOOK, + "govreport": QUERY_PROMPT_GOV, + "squality": QUERY_PROMPT_SQuALITY +} + + +QUERY_PROMPT_NORMAL = { + "qmsum": QUERY_PROMPT_QMSUM_NORMAL, + "wcep": QUERY_PROMPT_WCEP_NORMAL, + "booksum": QUERY_PROMPT_BOOK_NORMAL, + "govreport": QUERY_PROMPT_GOV_NORMAL, + "squality": QUERY_PROMPT_SQuALITY_NORMAL +} + + +if __name__ == '__main__': + pass diff --git a/retrieval.py b/retrieval.py new file mode 100644 index 0000000..7fa5a5f --- /dev/null +++ b/retrieval.py @@ -0,0 +1,90 @@ +import torch +import faiss +from transformers import AutoTokenizer, AutoModel + +from utils import show_time + + +def get_dense_retriever(retriever): + if retriever == 'contriever': + query_tokenizer = ctx_tokenizer = AutoTokenizer.from_pretrained('facebook/contriever') + query_encoder = ctx_encoder = AutoModel.from_pretrained('facebook/contriever') + else: + raise Exception("Retriever Error") + + return query_tokenizer, ctx_tokenizer, query_encoder, ctx_encoder + + +def split_batch(instructions, batch_size): + batch_instructions = [] + sub_batch = [] + for ind, ins in enumerate(instructions): + if ind != 0 and ind % batch_size == 0: + batch_instructions.append(sub_batch) + sub_batch = [ins] + else: + sub_batch.append(ins) + + if len(sub_batch) != 0: + batch_instructions.append(sub_batch) + + return batch_instructions + + +def get_dense_embedding(instructions, retriever, tokenizer, model, trunc_len=512, batch_size=64): + emb_list = [] + batch_instructions = split_batch(instructions, batch_size=batch_size) + for sub_batch in batch_instructions: + if retriever == 'contriever': + inputs = tokenizer(sub_batch, padding=True, truncation=True, return_tensors='pt', max_length=trunc_len).to(model.device) + with torch.no_grad(): + outputs = model(**inputs) + def mean_pooling(token_embeddings, mask): + token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.) + sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None] + return sentence_embeddings + + embeddings = mean_pooling(outputs[0], inputs['attention_mask']) + for e in embeddings: + emb_list.append(e) + else: + raise Exception("Error") + + return emb_list + + +def dense_neiborhood_search(corpus_data, query_data, metric='ip', num=8): + xq = torch.vstack(query_data).cpu().numpy() + xb = torch.vstack(corpus_data).cpu().numpy() + dim = xb.shape[1] + if metric == 'l2': + index = faiss.IndexFlatL2(dim) + elif metric == 'ip': + index = faiss.IndexFlatIP(dim) + xq = xq.astype('float32') + xb = xb.astype('float32') + faiss.normalize_L2(xq) + faiss.normalize_L2(xb) + else: + raise Exception("Index Metric Not Exist") + index.add(xb) + D, I = index.search(xq, num) + + return I[0] + + +def run_dense_retrieval(query_embedding, ch_text_chunk_embed, ch_text_chunk, chunk_num=4): + print("{} Dense Retrieval...".format(show_time())) + neib_ini = dense_neiborhood_search(ch_text_chunk_embed, query_embedding, num=chunk_num) + neib_ini = list(neib_ini) + + print("{} Retrieved Chunks:".format(show_time()), neib_ini) + retrieve_text = [] + for ind in neib_ini: + retrieve_text.append(ch_text_chunk[ind]) + + return neib_ini, retrieve_text + + +if __name__ == '__main__': + pass diff --git a/sum_eval.py b/sum_eval.py new file mode 100644 index 0000000..038d3c4 --- /dev/null +++ b/sum_eval.py @@ -0,0 +1,92 @@ +import argparse + +from tqdm import tqdm +from bert_score import score +from rouge_score import rouge_scorer + +from utils import * + + +def bert_score_eval(generate_response, ground_truth, device, batch_size=8): + P, R, F = score(generate_response, ground_truth, model_type="microsoft/deberta-xlarge-mnli", device=device, + batch_size=batch_size) + P = [float(i) for i in P.numpy()] + R = [float(i) for i in R.numpy()] + F = [float(i) for i in F.numpy()] + + return P, R, F + + +def rouge_eval(generate_response, ground_truth, type='rougeL'): + if not isinstance(ground_truth, str): + num_ref = len(ground_truth) + generate_response_expand = [generate_response] * num_ref + ground_truth_expand = ground_truth + Ps = [] + Rs = [] + Fs = [] + for i, j in zip(generate_response_expand, ground_truth_expand): + scorer = rouge_scorer.RougeScorer([type], use_stemmer=True) + scores = scorer.score(prediction=i, target=j) + Ps.append(scores[type].precision) + Rs.append(scores[type].recall) + Fs.append(scores[type].fmeasure) + P = max(Ps) + R = max(Rs) + F = max(Fs) + + return float(P), float(R), float(F) + else: + scorer = rouge_scorer.RougeScorer([type], use_stemmer=True) + scores = scorer.score(prediction=generate_response, target=ground_truth) + P = scores[type].precision + R = scores[type].recall + F = scores[type].fmeasure + + return float(P), float(R), float(F) + + +def response_eval(generate_responses, ground_truthes): + metric_list = [] + for ind, (generate_response, ground_truth) in enumerate(tqdm(zip(generate_responses, ground_truthes))): + metrics = dict() + _, _, rouge_L_F = rouge_eval(generate_response, ground_truth, type='rougeL') + _, _, rouge_1_F = rouge_eval(generate_response, ground_truth, type='rouge1') + _, _, rouge_2_F = rouge_eval(generate_response, ground_truth, type='rouge2') + metrics["ROUGE-L"] = {"F": rouge_L_F} + metrics["ROUGE-1"] = {"F": rouge_1_F} + metrics["ROUGE-2"] = {"F": rouge_2_F} + metric_list.append(metrics) + + all_metrics = dict() + for key in metric_list[0].keys(): + all_metrics[key] = {kk: float(np.mean([vv[key][kk] for vv in metric_list])) for kk in + metric_list[0][key].keys()} + + print("\n") + print(text_wrap("=" * 50 + "Final Evaluation" + "=" * 50)) + print_metrics(all_metrics) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--file_name", type=str, required=True) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--cuda", type=int, default=0) + opt = parser.parse_args() + FILE_NAME = opt.file_name + SEED = opt.seed + set_seed(int(SEED)) + DEVICE = get_device(int(opt.cuda)) + + with open(FILE_NAME, 'r') as file: + doc_data = json.load(file) + + print("{} #Test Data: {}".format(show_time(), len(doc_data))) + + generate_responses, ground_truthes = [], [] + for q, v in doc_data.items(): + generate_responses.append(v["response"]) + ground_truthes.append(v["gt"]) + + response_eval(generate_responses=generate_responses, ground_truthes=ground_truthes) diff --git a/train.py b/train.py new file mode 100644 index 0000000..03214d9 --- /dev/null +++ b/train.py @@ -0,0 +1,248 @@ +import argparse + +import torch.nn as nn +import dgl +from dgl.nn.pytorch.conv import GATConv +from dgl.nn.pytorch import JumpingKnowledge + +from utils import * + + +class GAT(nn.Module): + def __init__(self, in_dim, h_feats, dropout, attn_drop, n_head=4, num_layer=2): + super(GAT, self).__init__() + self.num_layer = num_layer + self.n_head = n_head + self.gat_layers = nn.ModuleList() + self.norm_layers = nn.ModuleList() + self.act_layers = nn.ModuleList() + self.gat_layers.append( + GATConv(in_dim, h_feats, num_heads=n_head, feat_drop=dropout, attn_drop=attn_drop, residual=False, + activation=None, allow_zero_in_degree=False)) + self.norm_layers.append(nn.BatchNorm1d(h_feats * n_head)) + self.act_layers.append(nn.PReLU(h_feats * n_head)) + for _ in range(num_layer - 1): + self.gat_layers.append( + GATConv(h_feats * n_head, h_feats, num_heads=n_head, feat_drop=dropout, attn_drop=attn_drop, + residual=False, activation=None, allow_zero_in_degree=False)) + self.norm_layers.append(nn.BatchNorm1d(h_feats * n_head)) + self.act_layers.append(nn.PReLU(h_feats * n_head)) + + self.JKN = JumpingKnowledge(mode='max') + + def forward(self, g, in_feat): + h = in_feat + hidden_list = [] + for l in range(self.num_layer): + h = self.gat_layers[l](g, h).reshape(in_feat.shape[0], -1) + h = self.norm_layers[l](h) + h = self.act_layers[l](h) + hidden_list.append(torch.mean(h.reshape(in_feat.shape[0], self.n_head, -1), dim=1)) + + ret = self.JKN(hidden_list) + + return ret + + +class GoR(nn.Module): + def __init__( + self, + in_dim: int = 768, + num_hidden: int = 768, + num_layer: int = 2, + n_head: int = 4, + feat_drop: float = 0.2, + attn_drop: float = 0.1, + ): + super(GoR, self).__init__() + self.encoder = GAT(in_dim=in_dim, h_feats=num_hidden, dropout=feat_drop, attn_drop=attn_drop, n_head=n_head, + num_layer=num_layer) + + def lambda_mrr_loss(self, y_pred, y_true, padded_value_indicator=-1, reduction="mean"): + """ + y_pred: FloatTensor [bz, topk] + y_true: FloatTensor [bz, topk] + """ + y_pred = y_pred.clone() + y_true = y_true.clone() + + padded_mask = y_true == padded_value_indicator + y_pred[padded_mask] = float("-inf") + y_true[padded_mask] = float("-inf") + y_pred_sorted, indices_pred = y_pred.sort(descending=True, dim=-1) + true_sorted_by_preds = torch.gather(y_true, dim=1, index=indices_pred) + true_diffs = true_sorted_by_preds[:, :, None] - true_sorted_by_preds[:, None, :] + padded_pairs_mask = torch.isfinite(true_diffs) + padded_pairs_mask = padded_pairs_mask & (true_diffs > 0) + scores_diffs = (y_pred_sorted[:, :, None] - y_pred_sorted[:, None, :]).clamp(min=-50, max=50) + scores_diffs.masked_fill_(torch.isnan(scores_diffs), 0.) + scores_diffs_exp = torch.exp(-scores_diffs) + losses = torch.log(1. + scores_diffs_exp) + + if reduction == "sum": + loss = torch.sum(losses[padded_pairs_mask]) + elif reduction == "mean": + loss = torch.mean(losses[padded_pairs_mask]) + else: + raise ValueError("Reduction method can be either sum or mean") + + return loss + + def forward(self, g, x, query_embedding_list, bert_score_list): + node_rep = self.encoder(g, x) + node_rep = torch.split(node_rep, g.batch_num_nodes().cpu().numpy().tolist(), dim=0) + + cl_loss_all = 0 + ranking_loss_all = 0 + entropy_all = 0 + """ + Note: We use a for loop to process each graph to avoid OOM. In GoR's training pipeline, there are actually two + "batch sizes", one is graph-level batch and the other is query-level batch. If the following for loop is + parallelized, the equivalent batch size is the product of the above two batch sizes, which is large and will + cause OOM on our computing devices. Nevertheless, if you have enough GPU Memory, you can parallelize it to + enable faster training. + """ + for ind, (single_rep, query_embedding, bert_score) in enumerate( + zip(node_rep, query_embedding_list, bert_score_list)): + bert_score = bert_score.to(x.device) + q = query_embedding.to(x.device) + _, bert_sorted_idx = bert_score.sort(dim=-1, descending=True) + p = single_rep[bert_sorted_idx[:, :1]] + n = single_rep[bert_sorted_idx[:, 1:]] + in_batch_neg_rep = torch.concat(node_rep[:ind] + node_rep[ind + 1:], dim=0).unsqueeze(0).repeat(p.shape[0], + 1, 1) + n = torch.concat([n, in_batch_neg_rep], dim=1) + q = q.unsqueeze(1) + p_sim = torch.matmul(q, p.transpose(1, 2)).squeeze(1) + n_sim = torch.matmul(q, n.transpose(1, 2)).squeeze(1) + ranking_list = torch.concat([p_sim, n_sim], dim=-1) + rank_score_prediction = ranking_list[:, :bert_sorted_idx.shape[-1]] + rank_gt = 1 / torch.arange(1, 1 + rank_score_prediction.shape[-1]).view(1, -1).repeat( + rank_score_prediction.shape[0], 1).to(x.device) + ranking_loss_all += self.lambda_mrr_loss(rank_score_prediction, rank_gt) + p_sim = torch.exp(p_sim / 1.0).sum(dim=-1) + n_sim = torch.exp(n_sim / 1.0).sum(dim=-1) + loss_cl = -torch.log(p_sim / (p_sim + n_sim)) + loss_cl = loss_cl.mean() + cl_loss_all += loss_cl + entropy_all += torch.distributions.Categorical( + torch.softmax(torch.matmul(q.squeeze(1), single_rep.T), dim=-1)).entropy().mean() + + cl_loss_all /= len(query_embedding_list) + ranking_loss_all /= len(query_embedding_list) + entropy_all /= len(query_embedding_list) + + return cl_loss_all, ranking_loss_all, entropy_all + + +def train_gor(train_dataloader): + model = GoR(in_dim=IN_DIM, num_hidden=HIDDEN_DIM, num_layer=NUM_LAYER, n_head=N_HEAD, feat_drop=DROPOUT) + model.to(DEVICE) + num_steps = len(train_dataloader) * MAX_EPOCH + optimizer = torch.optim.Adam(model.parameters(), lr=LR) + scheduler = lambda step: (1 + np.cos((step) * np.pi / num_steps)) * 0.5 + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=scheduler) + for e in range(MAX_EPOCH): + model.train() + epoch_loss = 0 + entropy_loss = 0 + for batch_id, (g, query_embedding_l, bert_score_l) in enumerate(train_dataloader): + g = g.to(DEVICE) + cl_loss, ranking_loss, entropy = model(g, g.ndata['feat'], query_embedding_l, bert_score_l) + loss = cl_loss + COE * ranking_loss + optimizer.zero_grad() + loss.backward() + optimizer.step() + if scheduler is not None: + scheduler.step() + epoch_loss += loss.detach().cpu() + entropy_loss += entropy.detach().cpu() + print('{} In epoch {}, lr: {:.5f}, loss: {:.4f}, entropy: {:.4f}'.format(show_time(), e, + optimizer.param_groups[0]['lr'], + float(epoch_loss / len( + train_dataloader)), float( + entropy_loss / len(train_dataloader)))) + + check_path("./weights") + torch.save(model.state_dict(), "./weights/{}.pth".format(DATASET)) + + +class GraphDataloader(dgl.data.DGLDataset): + def __init__(self, query_embedding_list, gs_list, bert_score_list): + self.query_embedding_list = query_embedding_list + self.gs_list = gs_list + self.bert_score_list = bert_score_list + super(GraphDataloader, self).__init__(name="GraphDataloader") + + def process(self): + pass + + def __getitem__(self, index): + return self.gs_list[index], self.query_embedding_list[index], self.bert_score_list[index] + + def __len__(self): + return int(len(self.gs_list)) + + +def mix_collate_fn(batch): + graph_data, query_embedding, bert_score = list(zip(*batch)) + graph_data = np.array(graph_data).flatten() + graph_data = [dgl.add_self_loop(i) for i in graph_data] + graph_data = dgl.batch(graph_data) + + query_embedding = [torch.vstack(q) for q in query_embedding] + bert_score = [torch.from_numpy(bs) for bs in bert_score] + + return graph_data, query_embedding, bert_score + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--dataset", type=str, required=True) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--cuda", type=int, default=0) + parser.add_argument("--batch_size", type=int, default=32) + parser.add_argument("--max_epoch", type=int, default=150) + parser.add_argument("--lr", type=float, default=1e-3) + parser.add_argument("--in_dim", type=int, default=768) + parser.add_argument("--hidden_dim", type=int, default=768) + parser.add_argument("--num_layer", type=int, default=2) + parser.add_argument("--n_head", type=int, default=4) + parser.add_argument("--drop", type=float, default=-1) + parser.add_argument("--coe", type=float, default=-1) + opt = parser.parse_args() + DATASET = opt.dataset + SEED = opt.seed + DROPOUT = opt.drop + COE = opt.coe + BATCH_SIZE = opt.batch_size + MAX_EPOCH = opt.max_epoch + LR = opt.lr + IN_DIM = opt.in_dim + HIDDEN_DIM = opt.hidden_dim + NUM_LAYER = opt.num_layer + N_HEAD = opt.n_head + + hyper_configuration = { + "qmsum": {"dropout": 0.2, "coe": 0.9}, + "wcep": {"dropout": 0.1, "coe": 0.7}, + "booksum": {"dropout": 0.2, "coe": 0.2}, + "govreport": {"dropout": 0.5, "coe": 0.7}, + "squality": {"dropout": 0.1, "coe": 0.4}, + } + + DROPOUT = hyper_configuration[DATASET]["dropout"] if DROPOUT == -1 else DROPOUT + COE = hyper_configuration[DATASET]["coe"] if COE == -1 else COE + + set_seed(int(SEED)) + DEVICE = get_device(int(opt.cuda)) + + gs_list, _ = dgl.load_graphs("./training_data/{}_gs.dgl".format(DATASET)) + query_embedding_list = read_from_pkl(output_file="./training_data/{}_qe.pkl".format(DATASET)) + bert_score_list = read_from_pkl(output_file="./training_data/{}_bs.pkl".format(DATASET)) + + train_dataset = GraphDataloader(query_embedding_list=query_embedding_list, gs_list=gs_list, + bert_score_list=bert_score_list) + train_dataloader = dgl.dataloading.GraphDataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, + collate_fn=mix_collate_fn, num_workers=0, pin_memory=True) + train_gor(train_dataloader=train_dataloader) diff --git a/training_preparation.py b/training_preparation.py new file mode 100644 index 0000000..18da848 --- /dev/null +++ b/training_preparation.py @@ -0,0 +1,108 @@ +import argparse + +import dgl +from tqdm import tqdm +from langchain_text_splitters import TokenTextSplitter + +from retrieval import * +from utils import * +from sum_eval import bert_score_eval +from data_process import get_processed_data, split_corpus_by_doc + + +def training_data_generation(graph, training_data): + queries = [i["query"] for i in training_data] + queries_embedding = get_dense_embedding(queries, retriever=RETRIEVER, tokenizer=QUERY_TOKENIZER, + model=QUERY_ENCODER) + queries_embedding = [i.cpu() for i in queries_embedding] + bert_score = None + if "answer" in training_data[0]: + responses = [] + for node, attrs in graph.nodes(data=True): + responses.append(node) + answers = [] + for i in training_data: + answers.extend([i["answer"]] * len(responses)) + responses = responses * len(training_data) + _, _, bert_score = bert_score_eval(generate_response=responses, ground_truth=answers, device=DEVICE) + bert_score = np.array(bert_score).reshape((len(training_data), -1)) + # print(bert_score.shape) + + return queries_embedding, bert_score + + +def integrate_isolated(graph, dgl_graph, all_doc_chunk_list, all_doc_chunk_list_embedding): + raw_chunk = [] + for node, attrs in graph.nodes(data=True): + raw_chunk.append(node) + non_dup_chunk = [] + non_dup_chunk_embedding = [] + for chunk, chunk_embedding in zip(all_doc_chunk_list, all_doc_chunk_list_embedding): + if chunk not in raw_chunk: + graph.add_node(chunk) + raw_chunk.append(chunk) + non_dup_chunk.append(chunk) + non_dup_chunk_embedding.append(chunk_embedding) + + if len(non_dup_chunk) != 0: + dgl_graph.add_nodes(num=len(non_dup_chunk), data={'feat': torch.vstack(non_dup_chunk_embedding).cpu()}) + + return graph, dgl_graph + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--dataset", type=str, required=True) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--cuda", type=int, default=0) + parser.add_argument("--retriever", type=str, default="contriever") + parser.add_argument("--chunk_size", type=int, default=256) + parser.add_argument("--chunk_overlap", type=int, default=32) + parser.add_argument("--recall_chunk_num", type=int, default=6) + opt = parser.parse_args() + DATASET = opt.dataset + SEED = opt.seed + RETRIEVER = opt.retriever + CHUNK_SIZE = opt.chunk_size + CHUNK_OVERLAP = opt.chunk_overlap + RECALL_CHUNK_NUM = opt.recall_chunk_num + + set_seed(int(SEED)) + DEVICE = get_device(int(opt.cuda)) + + QUERY_TOKENIZER, CTX_TOKENIZER, QUERY_ENCODER, CTX_ENCODER = get_dense_retriever(retriever=RETRIEVER) + QUERY_ENCODER = QUERY_ENCODER.to(DEVICE) + CTX_ENCODER = CTX_ENCODER.to(DEVICE) + + TEXT_SPLITTER = TokenTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP) + + data = get_processed_data(dataset=DATASET, train=True) + print("{} #Data: {}".format(show_time(), len(data))) + data = data[:400] + query_embedding_list = [] + bert_score_list = [] + gs_list = [] + for ind, sample in tqdm(enumerate(data), total=len(data)): + all_doc_chunk_list = split_corpus_by_doc(dataset=DATASET, sample=sample, text_splitter=TEXT_SPLITTER) + all_doc_chunk_list_embedding = get_dense_embedding(all_doc_chunk_list, retriever=RETRIEVER, + tokenizer=CTX_TOKENIZER, + model=CTX_ENCODER) + try: + graph = load_nx(path="./graph/{}_graph_{}.graphml".format(DATASET, ind)) + gs, _ = dgl.load_graphs("./graph/{}_graph_{}.dgl".format(DATASET, ind)) + dgl_graph = gs[0] + training_data = read_from_pkl(output_file="./graph/{}_training_data_{}.pkl".format(DATASET, ind)) + except Exception as e: + print(e) + continue + graph, dgl_graph = integrate_isolated(graph=graph, dgl_graph=dgl_graph, all_doc_chunk_list=all_doc_chunk_list, + all_doc_chunk_list_embedding=all_doc_chunk_list_embedding) + queries_embedding, bert_score = training_data_generation(graph=graph, training_data=training_data) + gs_list.append(dgl_graph) + query_embedding_list.append(queries_embedding) + bert_score_list.append(bert_score) + + check_path("./training_data") + dgl.save_graphs("./training_data/{}_gs.dgl".format(DATASET), gs_list) + write_to_pkl(data=query_embedding_list, output_file="./training_data/{}_qe.pkl".format(DATASET)) + write_to_pkl(data=bert_score_list, output_file="./training_data/{}_bs.pkl".format(DATASET)) diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..cda0c5a --- /dev/null +++ b/utils.py @@ -0,0 +1,125 @@ +import os +import json +import pickle +import time +import datetime +import random + +import torch +import openai +import networkx as nx +import numpy as np + + +def show_time(): + time_stamp = '\033[1;31;40m[' + str(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')) + ']\033[0m' + + return time_stamp + + +def get_device(index=0): + return torch.device("cuda:" + str(index) if torch.cuda.is_available() else "cpu") + + +def text_wrap(text): + return '\033[1;31;40m' + str(text) + '\033[0m' + + +def load_nx(path) -> nx.Graph: + return nx.read_graphml(path) + + +def store_nx(nx_obj, path): + nx.write_graphml_lxml(nx_obj, path) + + +def write_to_json(data, output_file): + with open(output_file, 'w', encoding='utf-8') as file: + json.dump(data, file, indent=4) + + +def write_to_pkl(data, output_file): + with open(output_file, 'wb') as file: + pickle.dump(data, file) + + +def read_from_pkl(output_file): + with open(output_file, 'rb') as file: + data = pickle.load(file) + + return data + + +def check_path(path): + if not os.path.exists(path): + os.mkdir(path) + + +def print_metrics(metrics): + for k, v in metrics.items(): + ff = "{} " + k + " (" + metric = metrics[k] + for sub_k in metric.keys(): + ff += sub_k + "/" + ff = ff[:-1] + "): " + for sub_v in metric.values(): + ff += format(sub_v, ".4f") + "/" + ff = ff[:-1] + print(ff.format(show_time())) + + +def set_seed(seed=42): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def get_llm_response_via_api(prompt, + API_BASE="https://api.together.xyz", + API_KEY="[YOUR_API_KEY]", + LLM_MODEL="mistralai/Mixtral-8x7B-Instruct-v0.1", + TAU=1.0, + TOP_P=1.0, + N=1, + SEED=42, + MAX_TRIALS=5, + TIME_GAP=5): + ''' + res = get_llm_response_via_api(prompt='hello') # Default: TAU Sampling (TAU=1.0) + res = get_llm_response_via_api(prompt='hello', TAU=0) # Greedy Decoding + res = get_llm_response_via_api(prompt='hello', TAU=0.5, N=2, SEED=None) # Return Multiple Responses w/ TAU Sampling + ''' + openai.api_base = API_BASE + openai.api_key = API_KEY + completion = None + while MAX_TRIALS: + MAX_TRIALS -= 1 + try: + completion = openai.ChatCompletion.create( + model=LLM_MODEL, + messages=[ + {"role": "user", "content": prompt} + ], + n=N, + temperature=TAU, + top_p=TOP_P, + seed=SEED, + ) + break + except Exception as e: + print(e) + print("Retrying...") + time.sleep(TIME_GAP) + + if completion is None: + raise Exception("Reach MAX_TRIALS={}".format(MAX_TRIALS)) + contents = completion.choices + if len(contents) == 1: + return contents[0].message["content"] + else: + return [c.message["content"] for c in contents] + + +if __name__ == '__main__': + pass

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