# Inspired by conll2003 dataset
# https://huggingface.co/datasets/conll2003


# 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
"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""

import os

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
  title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
  author = "Tjong Kim Sang, Erik F.  and
    De Meulder, Fien",
  booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
  year = "2003",
  url = "https://www.aclweb.org/anthology/W03-0419",
  pages = "142--147",
}
"""

_DESCRIPTION = """\
The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
not belong to the previous three groups.

The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
tagging scheme, whereas the original dataset uses IOB1.

For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
"""

_URL = "https://data.deepai.org/conll2003.zip"
_TRAINING_FILE = "train.txt"
_DEV_FILE = "valid.txt"
_TEST_FILE = "test.txt"


# class Conll2003Config(datasets.BuilderConfig):
#     """BuilderConfig for Conll2003"""

#     def __init__(self, **kwargs):
#         """BuilderConfig forConll2003.

#         Args:
#           **kwargs: keyword arguments forwarded to super.
#         """
#         super(Conll2003Config, self).__init__(**kwargs)


class Conll2003(datasets.GeneratorBasedBuilder):
    # """Conll2003 dataset."""

    # BUILDER_CONFIGS = [
    #     Conll2003Config(name="conll2003", version=datasets.Version(
    #         "1.0.0"), description="Conll2003 dataset"),
    # ]

    VERSION = datasets.Version("1.1.0")
    DEFAULT_CONFIG_NAME = "first_domain"
    BUILDER_CONFIGS = [
        # datasets.BuilderConfig(name="first_domain", version=VERSION,
        #                        description="This part of my dataset covers a first domain"),
        # datasets.BuilderConfig(name="second_domain", version=VERSION,
        #                        description="This part of my dataset covers a second domain"),
    ]

    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-PER",
                                "I-PER",
                                "B-TAG",
                                "I-TAG",
                                "B-LOC",
                                "I-LOC",
                                "B-TIME",
                                "I-TIME",
                                "B-SORT",
                                "I-SORT"
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://www.aclweb.org/anthology/W03-0419/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # downloaded_file = dl_manager.download_and_extract(_URL)
        # data_files = {
        #     "train": os.path.join(downloaded_file, _TRAINING_FILE),
        #     "dev": os.path.join(downloaded_file, _DEV_FILE),
        #     "test": os.path.join(downloaded_file, _TEST_FILE),
        # }

        # data_files = {
        #     "train": os.path.join(downloaded_file, _TRAINING_FILE),
        #     "dev": os.path.join(downloaded_file, _DEV_FILE),
        #     "test": os.path.join(downloaded_file, _TEST_FILE),
        # }

        url = "https://pastebin.pl/view/raw/f1bffd94"
        text_file = dl_manager.download(url)

        data_files = {
            "train": text_file,
            "dev": text_file,
            "test": text_file,
        }

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

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            pos_tags = []
            chunk_tags = []
            ner_tags = []
            for line in f:
                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    # conll2003 tokens are space separated
                    splits = line.split(" ")
                    tokens.append(splits[0])
                    ner_tags.append(splits[1].rstrip())
            # last example
            if tokens:
                yield guid, {
                    "id": str(guid),
                    "tokens": tokens,
                    "ner_tags": ner_tags,
                }