# coding=utf-8
# Copyright 2020 The HuggingFace datasets Authors, DataLab 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.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Poem Sentiment: A sentiment dataset of poem verses"""


import datalabs
from datalabs import get_task, TaskType

_CITATION = """\
@misc{sheng2020investigating,
      title={Investigating Societal Biases in a Poetry Composition System},
      author={Emily Sheng and David Uthus},
      year={2020},
      eprint={2011.02686},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg. \
This dataset can be used for tasks such as sentiment classification or style transfer for poems.
"""


_HOMEPAGE = "https://github.com/google-research-dataset/poem-sentiment"

_BASE_URL = "https://raw.githubusercontent.com/google-research-datasets/poem-sentiment/master/data/"
_URLS = {
    "train": f"{_BASE_URL}/train.tsv",
    "dev": f"{_BASE_URL}/dev.tsv",
    "test": f"{_BASE_URL}/test.tsv",
}
_LABEL_MAPPING = {-1: 0, 0: 2, 1: 1, 2: 3}


class PoemSentiment(datalabs.GeneratorBasedBuilder):
    """Poem Sentiment: A sentiment dataset of poem verses"""

    VERSION = datalabs.Version("1.0.0")

    def _info(self):
        return datalabs.DatasetInfo(
            description=_DESCRIPTION,
            features=datalabs.Features(
                {
                    "id": datalabs.Value("int32"),
                    "text": datalabs.Value("string"),
                    "label": datalabs.ClassLabel(
                        names=["negative", "positive", "neutral", "mixed"]
                    ),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[
                get_task(TaskType.sentiment_classification)(
                    text_column="text", label_column="label"
                )
            ],
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download(_URLS)
        return [
            datalabs.SplitGenerator(
                name=datalabs.Split.TRAIN,
                gen_kwargs={"filepath": downloaded_files["train"]},
            ),
            datalabs.SplitGenerator(
                name=datalabs.Split.VALIDATION,
                gen_kwargs={"filepath": downloaded_files["dev"]},
            ),
            datalabs.SplitGenerator(
                name=datalabs.Split.TEST,
                gen_kwargs={"filepath": downloaded_files["test"]},
            ),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, encoding="utf-8") as f:
            lines = f.readlines()
            for line in lines:
                fields = line.strip().split("\t")
                idx, verse_text, label = fields
                label = _LABEL_MAPPING[int(label)]
                yield int(idx), {"id": int(idx), "text": verse_text, "label": label}
