# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


_CITATION = """\
@inproceedings{puduppully-etal-2019-data,
    title = "Data-to-text Generation with Entity Modeling",
    author = "Puduppully, Ratish  and
      Dong, Li  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1195",
    doi = "10.18653/v1/P19-1195",
    pages = "2023--2035",
}
"""

_DESCRIPTION = """\
The MLB dataset for data to text generation contains Major League Baseball games statistics and 
their human-written summaries. 
"""

_HOMEPAGE = "https://github.com/ratishsp/mlb-data-scripts"

_LICENSE = ""

_URLs = {
    "train": "train.jsonl.bz2",
    "validation": "validation.jsonl.bz2",
    "test": "test.jsonl.bz2"
}


class MlbDataToText(datasets.GeneratorBasedBuilder):
    """MLB dataset for data to text generation"""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "home_name": datasets.Value("string"),
                "box_score": dict,
                "home_city": datasets.Value("string"),
                "vis_name": datasets.Value("string"),
                "play_by_play": dict,
                "vis_line": dict,
                "vis_city": datasets.Value("string"),
                "day": datasets.Value("string"),
                "home_line": dict,
                "summary": list,
                "gem_id": datasets.Value("string")
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        data_dir = dl_manager.download_and_extract(_URLs)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["test"],
                    "split": "test"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["validation"],
                    "split": "validation",
                },
            ),
        ]

    def _generate_examples(
        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                data = json.loads(row)
                yield id_, {
                    "home_name": data["home_name"],
                    "box_score": data["box_score"],
                    "home_city": data["home_city"],
                    "vis_name": data["vis_name"],
                    "play_by_play": data["play_by_play"],
                    "vis_line": data["vis_line"],
                    "vis_city": data["vis_city"],
                    "day": data["day"],
                    "home_line": data["home_line"],
                    "summary": data["summary"],
                    "gem_id": data["gem_id"]
                }