# 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
#
# 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.

# Lint as: python3
"""The Open Threat dataset"""


import datasets


logger = datasets.logging.get_logger(__name__)

_DESCRIPTION = """\
TBD
"""

_CITATION = """\
TBD
"""


_URL = "https://huggingface.co/datasets/priamai/openthreatner/raw/main/conll/"
_TRAINING_FILE = "text_32.conll"
_DEV_FILE = "text_23.conll"
_TEST_FILE = "text_56.conll"

class OurDatasetConfig(datasets.BuilderConfig):
    """The Open NER dataset."""

    def __init__(self, **kwargs):
        """BuilderConfig for Open Threat dataset.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(OurDatasetConfig, self).__init__(**kwargs)


class OurDataset(datasets.GeneratorBasedBuilder):
    """The Open NER dataset Entities Dataset."""

    BUILDER_CONFIGS = [
        OurDatasetConfig(
            name="Open Threat", version=datasets.Version("1.0.0"), description="The Open Cyber Threat Entities Dataset"
        ),
    ]

    def _info(self):

        our_names = [
                "O",
                "B-date",
                "I-date",
                "B-time",
                "I-time",
                "B-geo_location",
                "I-geo_location",
                "B-organization",
                "I-organization",
                "B-sector",
                "I-sector",
                "B-threat_actor",
                "I-threat_actor",
                "B-exploit_name",
                "I-exploit_name",
                "B-malware",
                "I-malware",
                "B-os",
                "I-os",
                "B-software",
                "I-software",
                "B-hardware",
                "I-hardware",
                "B-username",
                "I-username",
                "B-ttp",
                "I-ttp",
                "B-code_cmd",
                "I-code_cmd",
                "B-classification",
                "I-classification",
            ]

        logger.info("Total names = %d" % len(our_names))

        dinfo = datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names= list(map(str.upper,our_names))
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://test.cti.tools/",
            citation=_CITATION,
        )

        logger.info(dinfo)
        
        return dinfo

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "dev": f"{_URL}{_DEV_FILE}",
            "test": f"{_URL}{_TEST_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            current_tokens = []
            current_labels = []
            sentence_counter = 0
            for row in f:
                row = row.rstrip()
                if row:
                    token, label = row.split("\t")
                    current_tokens.append(token)
                    current_labels.append(label)
                else:
                    # New sentence
                    if not current_tokens:
                        # Consecutive empty lines will cause empty sentences
                        continue
                    assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
                    sentence = (
                        sentence_counter,
                        {
                            "id": str(sentence_counter),
                            "tokens": current_tokens,
                            "ner_tags": current_labels,
                        },
                    )
                    sentence_counter += 1
                    current_tokens = []
                    current_labels = []
                    yield sentence
            # Don't forget last sentence in dataset 🧐
            if current_tokens:
                yield sentence_counter, {
                    "id": str(sentence_counter),
                    "tokens": current_tokens,
                    "ner_tags": current_labels,
                }