# 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
import datasets


_DESCRIPTION = ""
_HOMEPAGE_URL = ""
_CITATION = None
_TRAIN_URL = "https://huggingface.co/datasets/ayuhamaro/ner-model-tune/raw/main/train"


class NlpModelTune(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return 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=[
                                "O",
                                "B-CARDINAL",
                                "B-DATE",
                                "B-EVENT",
                                "B-FAC",
                                "B-GPE",
                                "B-LANGUAGE",
                                "B-LAW",
                                "B-LOC",
                                "B-MONEY",
                                "B-NORP",
                                "B-ORDINAL",
                                "B-ORG",
                                "B-PERCENT",
                                "B-PERSON",
                                "B-PRODUCT",
                                "B-QUANTITY",
                                "B-TIME",
                                "B-WORK_OF_ART",
                                "I-CARDINAL",
                                "I-DATE",
                                "I-EVENT",
                                "I-FAC",
                                "I-GPE",
                                "I-LANGUAGE",
                                "I-LAW",
                                "I-LOC",
                                "I-MONEY",
                                "I-NORP",
                                "I-ORDINAL",
                                "I-ORG",
                                "I-PERCENT",
                                "I-PERSON",
                                "I-PRODUCT",
                                "I-QUANTITY",
                                "I-TIME",
                                "I-WORK_OF_ART",
                                "E-CARDINAL",
                                "E-DATE",
                                "E-EVENT",
                                "E-FAC",
                                "E-GPE",
                                "E-LANGUAGE",
                                "E-LAW",
                                "E-LOC",
                                "E-MONEY",
                                "E-NORP",
                                "E-ORDINAL",
                                "E-ORG",
                                "E-PERCENT",
                                "E-PERSON",
                                "E-PRODUCT",
                                "E-QUANTITY",
                                "E-TIME",
                                "E-WORK_OF_ART",
                                "S-CARDINAL",
                                "S-DATE",
                                "S-EVENT",
                                "S-FAC",
                                "S-GPE",
                                "S-LANGUAGE",
                                "S-LAW",
                                "S-LOC",
                                "S-MONEY",
                                "S-NORP",
                                "S-ORDINAL",
                                "S-ORG",
                                "S-PERCENT",
                                "S-PERSON",
                                "S-PRODUCT",
                                "S-QUANTITY",
                                "S-TIME",
                                "S-WORK_OF_ART"
                            ]
                        )
                    ),
                },
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE_URL,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        train_path = dl_manager.download_and_extract(_TRAIN_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"data_path": train_path},
            )
        ]

    def _generate_examples(self, data_path):
        sentence_counter = 0
        with open(data_path, encoding="utf-8") as f:
            current_words = []
            current_labels = []
            for row in f:
                row = row.rstrip()
                row_split = row.split("\t")
                if len(row_split) == 2:
                    token, label = row_split
                    current_words.append(token)
                    current_labels.append(label)
                else:
                    if not current_words:
                        continue
                    assert len(current_words) == len(current_labels), "word len doesnt match label length"
                    sentence = (
                        sentence_counter,
                        {
                            "id": str(sentence_counter),
                            "tokens": current_words,
                            "ner_tags": current_labels,
                        },
                    )
                    sentence_counter += 1
                    current_words = []
                    current_labels = []
                    yield sentence

            # if something remains:
            if current_words:
                sentence = (
                    sentence_counter,
                    {
                        "id": str(sentence_counter),
                        "tokens": current_words,
                        "ner_tags": current_labels,
                    },
                )
                yield sentence