# 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.
"""Dataset builder script for the Odinsynth sequence generation dataset"""

import csv
import json
import os

import datasets

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
# _CITATION = """\
# @InProceedings{huggingface:dataset,
# title = {A great new dataset},
# author={huggingface, Inc.
# },
# year={2020}
# }
# """


# You can copy an official description
_DESCRIPTION = """
Dataset for Odinsynth sequence data generation
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

_LICENSE = ""

_URLS = {
    "synthetic_surface": "synthetic_surface.tar.gz"
}


class OdinsynthSequenceDataset(datasets.GeneratorBasedBuilder):
    """This contains a dataset for odinsynth rule synthesis in a supervised manner"""

    VERSION = datasets.Version("1.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="synthetic_surface",
                               version=VERSION,
                               description="Synthetic data with synthetic_surface rules only"),
    ]

    DEFAULT_CONFIG_NAME = "synthetic_surface"

    def _info(self):
        features = datasets.Features(
            {
                "rule": datasets.Value("string"),
                "spec": datasets.Sequence(
                    {
                        "sentence": datasets.Value("string"),
                        "matched_text": datasets.Value("string"),
                        "words": datasets.Sequence(datasets.Value("string")),
                        "match_start": datasets.Value("int16"),
                        "match_end": datasets.Value("int16"),
                    }
                ),
                "generation_info": [
                    {
                        "transitions": datasets.Sequence(datasets.Value("string")),
                        "generation_rules": datasets.Sequence(datasets.Value("string")),
                        "delexicalized_generation_rules": datasets.Sequence(datasets.Value("string")),
                        "innermost_substitutions": datasets.Sequence(datasets.Value("string")),
                    }
                ]

            }
        )

        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=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            # citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # 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_path = dl_manager.download_and_extract(_URLS[self.config.name])

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_path, "train.jsonl"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_path, "dev.jsonl"),
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_path, "test.jsonl"),
                    "split": "test",
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                yield key, {
                    "rule": data["rule"],
                    "spec": [
                        {
                            "sentence": s[0],
                            "matched_text": s[1],
                            "words": s[3],
                            "match_start": s[4],
                            "match_end": s[5]
                        }
                        for s in data['spec']
                    ],
                    "generation_info": [
                        {
                            "transitions": i["transitions"],
                            "generation_rules": i["generation_rules"],
                            "delexicalized_generation_rules": i["delexicalized_generation_rules"],
                            "innermost_substitutions": i["innermost_substitutions"],
                        }
                        for i in data['generation_info']
                    ]
                }