# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""Introduction to the Bio-Entity Recognition Task at JNLPBA"""

import glob
import os
import re

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{kim2004introduction,
               title={Introduction to the bio-entity recognition task at JNLPBA},
               author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},
               booktitle={Proceedings of the international joint workshop on natural language processing in biomedicine and its applications},
               pages={70--75},
               year={2004},
               organization={Citeseer}
}
"""

_DESCRIPTION = """\
The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search
on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts
were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification.
Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
"""

_HOMEPAGE = "http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004"
TRAIN_URL = "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz"
VAL_URL = "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Evaluation/Genia4ERtest.tar.gz"
TEST_URL = "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Tool/JNLPBA2004_eval.tar.gz"	


class JNLPBAConfig(datasets.BuilderConfig):
    """BuilderConfig for JNLPBA"""

    def __init__(self, **kwargs):
        """BuilderConfig for JNLPBA.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(JNLPBAConfig, self).__init__(**kwargs)


class JNLPBA(datasets.GeneratorBasedBuilder):
    """JNLPBA dataset."""

    BUILDER_CONFIGS = [
        JNLPBAConfig(name="jnlpba", version=datasets.Version("1.0.0"), description="JNLPBA dataset"),
    ]

    def _info(self):
        custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE',
            'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-CELL_LINE', 'I-CELL_LINE', 'B-CELL_TYPE', 'I-CELL_TYPE', 
            'B-PROTEIN', 'I-PROTEIN', 'B-SPECIES', 'I-SPECIES'] 
        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=custom_names 
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        train_files = dl_manager.download_and_extract(TRAIN_URL)
        val_files = dl_manager.download_and_extract(VAL_URL)
        # test_files = dl_manager.download_and_extract(TEST_URL)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_files}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_files}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": val_files}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        filenames = glob.glob(os.path.join(filepath, "Genia4ER*.iob2"))
        guid = 0
        for filename in filenames:
            with open(filename, encoding="utf-8") as f:
                if guid >= 0:
                    guid += 1  # update guid to avoid DuplicatedKeysError
                tokens = []
                ner_tags = []
                for line in f:
                    if len(re.split(r"###MEDLINE:", line)) == 2:
                        continue

                    elif line == "" or line == "\n":
                        if tokens:
                            # print(guid, line)
                            yield guid, {
                                "id": str(guid),
                                "tokens": tokens,
                                "ner_tags": ner_tags,
                            }
                            guid += 1
                            tokens = []
                            ner_tags = []

                    else:
                        # tokens are tab separated
                        splits = line.split("\t")
                        tokens.append(splits[0])
                        if(splits[1].rstrip()=="B-cell_line"):
                            ner_tags.append("B-CELL_LINE")
                        elif(splits[1].rstrip()=="I-cell_line"):
                            ner_tags.append("I-CELL_LINE")

                        elif(splits[1].rstrip()=="B-cell_type"):
                            ner_tags.append("B-CELL_TYPE")
                        elif(splits[1].rstrip()=="I-cell_type"):
                            ner_tags.append("I-CELL_TYPE")

                        elif(splits[1].rstrip()=="B-protein"):
                            ner_tags.append("B-PROTEIN")
                        elif(splits[1].rstrip()=="I-protein"):
                            ner_tags.append("I-PROTEIN")
                        else:
                            ner_tags.append(splits[1].rstrip())

                # last example
                yield guid, {
                    "id": str(guid),
                    "tokens": tokens,
                    "ner_tags": ner_tags,
                }