# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""The lingual SemEval2014 Task5 Reviews Corpus"""

import datasets

_CITATION = """\
@article{2014SemEval,
  title={SemEval-2014 Task 4: Aspect Based Sentiment Analysis},
  author={ Pontiki, M.  and D Galanis and  Pavlopoulos, J.  and  Papageorgiou, H.  and  Manandhar, S. },
  journal={Proceedings of International Workshop on Semantic Evaluation at},
  year={2014},
}
"""

_LICENSE = """\
    Please click on the homepage URL for license details.
"""

_DESCRIPTION = """\
A collection of SemEval2014 specifically designed to aid research in Aspect Based Sentiment Analysis.
"""

_CONFIG = [
    
    # restaurants domain
    "restaurants",
    # laptops domain
    "laptops",
]

_VERSION = "0.0.1"

_HOMEPAGE_URL = "https://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools"
_DOWNLOAD_URL = "https://raw.githubusercontent.com/YaxinCui/ABSADataset/main/SemEval2014Task4/{split}/{domain}_{split}.xml"


class SemEval2014Task4RawConfig(datasets.BuilderConfig):
    """BuilderConfig for SemEval2014Config."""

    def __init__(self, _CONFIG, **kwargs):
        super(SemEval2014Task4RawConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs),
        self.configs = _CONFIG


class SemEval2014Task4Raw(datasets.GeneratorBasedBuilder):
    """The lingual Amazon Reviews Corpus"""

    BUILDER_CONFIGS = [
        SemEval2014Task4RawConfig(
            name="All",
            _CONFIG=_CONFIG,
            description="A collection of SemEval2014 specifically designed to aid research in lingual Aspect Based Sentiment Analysis.",
        )
    ] + [
        SemEval2014Task4RawConfig(
            name=config,
            _CONFIG=[config],
            description=f"{config} of SemEval2014 specifically designed to aid research in Aspect Based Sentiment Analysis",
        )
        for config in _CONFIG
    ]
    
    BUILDER_CONFIG_CLASS = SemEval2014Task4RawConfig
    DEFAULT_CONFIG_NAME = "All"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {'text': datasets.Value(dtype='string'),
                'aspectTerms': [
                    {'from': datasets.Value(dtype='string'),
                    'polarity': datasets.Value(dtype='string'),
                    'term': datasets.Value(dtype='string'),
                    'to': datasets.Value(dtype='string')}
                ],
                'aspectCategories': [
                    {'category': datasets.Value(dtype='string'),
                    'polarity': datasets.Value(dtype='string')}
                ],
                'domain': datasets.Value(dtype='string'),
                'sentenceId': datasets.Value(dtype='string')
            }
            ),
            supervised_keys=None,
            license=_LICENSE,
            homepage=_HOMEPAGE_URL,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        train_urls = [_DOWNLOAD_URL.format(split="train", domain=config) for config in self.config.configs]
        dev_urls = [_DOWNLOAD_URL.format(split="trial", domain=config) for config in self.config.configs]
        test_urls = [_DOWNLOAD_URL.format(split="test", domain=config) for config in self.config.configs]

        train_paths = dl_manager.download_and_extract(train_urls)
        dev_paths = dl_manager.download_and_extract(dev_urls)
        test_paths = dl_manager.download_and_extract(test_urls)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"file_paths": train_paths, "domain_list": self.config.configs}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"file_paths": dev_paths, "domain_list": self.config.configs}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"file_paths": test_paths, "domain_list": self.config.configs}),
        ]

    def _generate_examples(self, file_paths, domain_list):
        row_count = 0
        assert len(file_paths)==len(domain_list)

        for i in range(len(file_paths)):
            file_path, domain = file_paths[i], domain_list[i]
            semEvalDataset = SemEvalXMLDataset(file_path, domain)

            for example in semEvalDataset.SentenceWithOpinions:
                yield row_count, example
                row_count += 1

from xml.dom.minidom import parse

class SemEvalXMLDataset():
    def __init__(self, file_name, domain):
        # 获得SentenceWithOpinions，一个List包含(reviewId, sentenceId, text, Opinions)

        self.SentenceWithOpinions = []
        self.xml_path = file_name

        self.sentenceXmlList = parse(open(self.xml_path)).getElementsByTagName('sentence')

        for sentenceXml in self.sentenceXmlList:
            
            sentenceId = sentenceXml.getAttribute("id")
            if len(sentenceXml.getElementsByTagName("text")[0].childNodes) < 1:
                # skip no reviews part
                continue
            text = sentenceXml.getElementsByTagName("text")[0].childNodes[0].nodeValue

            aspectTermsXLMList = sentenceXml.getElementsByTagName("aspectTerm")
            aspectTerms = []
            for opinionXml in aspectTermsXLMList:
                # some text maybe have no opinion
                term = opinionXml.getAttribute("term")
                polarity = opinionXml.getAttribute("polarity")
                from_ = opinionXml.getAttribute("from")
                to = opinionXml.getAttribute("to")
                aspectTermDict = {
                    "term": term,
                    "polarity": polarity,
                    "from": from_,
                    "to": to
                }
                aspectTerms.append(aspectTermDict)

            # 从小到大排序
            aspectTerms.sort(key=lambda x: int(x["from"]))

            aspectCategoriesXmlList = sentenceXml.getElementsByTagName("aspectCategory")
            aspectCategories = []
            for aspectCategoryXml in aspectCategoriesXmlList:
                category = aspectCategoryXml.getAttribute("category")
                polarity = aspectCategoryXml.getAttribute("polarity")
                aspectCategoryDict = {
                    "category": category,
                    "polarity": polarity
                }
                aspectCategories.append(aspectCategoryDict)

            self.SentenceWithOpinions.append({
                    "text": text, 
                    "aspectTerms": aspectTerms,
                    "aspectCategories": aspectCategories,
                    "domain": domain, 
                    "sentenceId": sentenceId
                    }
                )