# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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
#
# Unless required by applicable law or agreed to in writing, software
# 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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# limitations under the License.
"""NewsMTSC Dataset: (Multi-)Target-dependent Sentiment Classification in News Articles Dataset"""


import csv
import json
import os

import datasets

_CITATION = """\
@InProceedings{Hamborg2021b,
  author    = {Hamborg, Felix and Donnay, Karsten},
  title     = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
  year      = {2021},
  month     = {Apr.},
  location  = {Virtual Event},
}
"""
_DESCRIPTION = """\
NewsMTSC: A large, manually annotated dataset for target-dependent sentiment classification in English news articles.
"""
_HOMEPAGE = "https://github.com/fhamborg/NewsMTSC/"
_LICENSE = "MIT"
_URL = "https://raw.githubusercontent.com/fhamborg/NewsMTSC/a96f785fd3110c202e05e63579ddb30043eef128/NewsSentiment/experiments/default/datasets/"
_URLS = {
    "mt": {
        datasets.Split.TRAIN: _URL + "newsmtsc-mt/train.jsonl",
        datasets.Split.VALIDATION: _URL + "newsmtsc-mt/dev.jsonl",
        datasets.Split.TEST: _URL + "newsmtsc-mt/test.jsonl",
    },
    "rw": {
        datasets.Split.TRAIN: _URL + "newsmtsc-rw/train.jsonl",
        datasets.Split.VALIDATION: _URL + "newsmtsc-rw/dev.jsonl",
        datasets.Split.TEST: _URL + "newsmtsc-rw/test.jsonl",
    },
}


class AllowNoFurtherMentionsFeatures(datasets.Features):
    def encode_example(self, example):
        for target in example["targets"]:
            if "further_mentions" not in target:
                target["further_mentions"] = []
        return super().encode_example(example)


class NewsSentimentNewsmtsc(datasets.GeneratorBasedBuilder):
    """NewsMTSC Dataset: A large, manually annotated dataset for target-dependent sentiment classification in political
    news articles."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="mt", version=VERSION, description="Multi-target: sentences that contain at least two target mentions."),
        datasets.BuilderConfig(name="rw", version=VERSION, description="Real-world: aims that sentences resemble real-world distribution as to sentiment and other factors mentioned in the paper"),
    ]

    DEFAULT_CONFIG_NAME = "rw"

    def _info(self):
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=AllowNoFurtherMentionsFeatures(
                {
                    "primary_gid": datasets.Value("string"),
                    "sentence_normalized": datasets.Value("string"),
                    "targets": datasets.features.Sequence(
                        {
                            "Input.gid": datasets.Value("string"),
                            "from": datasets.Value("uint32"),
                            "to": datasets.Value("uint32"),
                            "mention": datasets.Value("string"),
                            "polarity": datasets.Value("float"),
                            "further_mentions": datasets.features.Sequence(
                                {
                                    "from": datasets.Value("uint32"),
                                    "to": datasets.Value("uint32"),
                                    "mention": datasets.Value("string"),
                                },
                            ),
                        },
                    ),
                },
            ),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        urls = _URLS[self.config.name]
        data_dir = dl_manager.download(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir[datasets.Split.TRAIN],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir[datasets.Split.TEST],
                    "split": "test"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir[datasets.Split.VALIDATION],
                    "split": "dev",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as f:
            for row in f:
                data = json.loads(row)
                if split == "test":
                    for target in data["targets"]:
                        target["polarity"] = None
                yield data["primary_gid"], data