# coding=utf-8
# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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from pathlib import Path
from typing import Dict, List

import datasets

from bigbio.utils import parsing, schemas
from bigbio.utils.configs import BigBioConfig
from bigbio.utils.constants import Lang, Tasks
from bigbio.utils.license import Licenses

_DATASETNAME = "bionlp_st_2011_id"
_DISPLAYNAME = "BioNLP 2011 ID"

_SOURCE_VIEW_NAME = "source"
_UNIFIED_VIEW_NAME = "bigbio"

_LANGUAGES = [Lang.EN]
_PUBMED = True
_LOCAL = False
_CITATION = """\
@inproceedings{pyysalo-etal-2011-overview,
    title = "Overview of the Infectious Diseases ({ID}) task of {B}io{NLP} Shared Task 2011",
    author = "Pyysalo, Sampo  and
      Ohta, Tomoko  and
      Rak, Rafal  and
      Sullivan, Dan  and
      Mao, Chunhong  and
      Wang, Chunxia  and
      Sobral, Bruno  and
      Tsujii, Jun{'}ichi  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of {B}io{NLP} Shared Task 2011 Workshop",
    month = jun,
    year = "2011",
    address = "Portland, Oregon, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W11-1804",
    pages = "26--35",
}
"""

_DESCRIPTION = """\
The dataset of the Infectious Diseases (ID) task of
BioNLP Shared Task 2011.
"""

_HOMEPAGE = "https://github.com/openbiocorpora/bionlp-st-2011-id"

_LICENSE = Licenses.GENIA_PROJECT_LICENSE

_URLs = {
    "source": "https://github.com/openbiocorpora/bionlp-st-2011-id/archive/refs/heads/master.zip",
    "bigbio_kb": "https://github.com/openbiocorpora/bionlp-st-2011-id/archive/refs/heads/master.zip",
}

_SUPPORTED_TASKS = [
    Tasks.EVENT_EXTRACTION,
    Tasks.COREFERENCE_RESOLUTION,
    Tasks.NAMED_ENTITY_RECOGNITION,
]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"


class bionlp_st_2011_id(datasets.GeneratorBasedBuilder):
    """The dataset of the Infectious Diseases (ID) task of
    BioNLP Shared Task 2011."""

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="bionlp_st_2011_id_source",
            version=SOURCE_VERSION,
            description="bionlp_st_2011_id source schema",
            schema="source",
            subset_id="bionlp_st_2011_id",
        ),
        BigBioConfig(
            name="bionlp_st_2011_id_bigbio_kb",
            version=BIGBIO_VERSION,
            description="bionlp_st_2011_id BigBio schema",
            schema="bigbio_kb",
            subset_id="bionlp_st_2011_id",
        ),
    ]

    DEFAULT_CONFIG_NAME = "bionlp_st_2011_id_source"

    _ROLE_MAPPING = {
        "Theme1": "Theme",
        "Theme2": "Theme",
        "Site1": "Site",
        "Site2": "Site",
    }

    def _info(self):
        """
        - `features` defines the schema of the parsed data set. The schema depends on the
        chosen `config`: If it is `_SOURCE_VIEW_NAME` the schema is the schema of the
        original data. If `config` is `_UNIFIED_VIEW_NAME`, then the schema is the
        canonical KB-task schema defined in `biomedical/schemas/kb.py`.
        """
        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document_id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "text_bound_annotations": [  # T line in brat, e.g. type or event trigger
                        {
                            "offsets": datasets.Sequence([datasets.Value("int32")]),
                            "text": datasets.Sequence(datasets.Value("string")),
                            "type": datasets.Value("string"),
                            "id": datasets.Value("string"),
                        }
                    ],
                    "events": [  # E line in brat
                        {
                            "trigger": datasets.Value(
                                "string"
                            ),  # refers to the text_bound_annotation of the trigger,
                            "id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            "arguments": datasets.Sequence(
                                {
                                    "role": datasets.Value("string"),
                                    "ref_id": datasets.Value("string"),
                                }
                            ),
                        }
                    ],
                    "relations": [  # R line in brat
                        {
                            "id": datasets.Value("string"),
                            "head": {
                                "ref_id": datasets.Value("string"),
                                "role": datasets.Value("string"),
                            },
                            "tail": {
                                "ref_id": datasets.Value("string"),
                                "role": datasets.Value("string"),
                            },
                            "type": datasets.Value("string"),
                        }
                    ],
                    "equivalences": [  # Equiv line in brat
                        {
                            "id": datasets.Value("string"),
                            "ref_ids": datasets.Sequence(datasets.Value("string")),
                        }
                    ],
                    "attributes": [  # M or A lines in brat
                        {
                            "id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            "ref_id": datasets.Value("string"),
                            "value": datasets.Value("string"),
                        }
                    ],
                    "normalizations": [  # N lines in brat
                        {
                            "id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            "ref_id": datasets.Value("string"),
                            "resource_name": datasets.Value(
                                "string"
                            ),  # Name of the resource, e.g. "Wikipedia"
                            "cuid": datasets.Value(
                                "string"
                            ),  # ID in the resource, e.g. 534366
                            "text": datasets.Value(
                                "string"
                            ),  # Human readable description/name of the entity, e.g. "Barack Obama"
                        }
                    ],
                },
            )
        elif self.config.schema == "bigbio_kb":
            features = schemas.kb_features

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=str(_LICENSE),
            citation=_CITATION,
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:

        my_urls = _URLs[self.config.schema]
        data_dir = Path(dl_manager.download_and_extract(my_urls))
        data_files = {
            "train": data_dir / f"bionlp-st-2011-id-master" / "original-data" / "train",
            "dev": data_dir / f"bionlp-st-2011-id-master" / "original-data" / "devel",
            "test": data_dir / f"bionlp-st-2011-id-master" / "original-data" / "test",
        }

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

    def _standardize_arguments_roles(self, kb_example: Dict) -> Dict:

        for event in kb_example["events"]:
            for argument in event["arguments"]:
                role = argument["role"]
                argument["role"] = self._ROLE_MAPPING.get(role, role)

        return kb_example

    def _generate_examples(self, data_files: Path):
        if self.config.schema == "source":
            txt_files = list(data_files.glob("*txt"))
            for guid, txt_file in enumerate(txt_files):
                example = parsing.parse_brat_file(txt_file)
                example["id"] = str(guid)
                yield guid, example
        elif self.config.schema == "bigbio_kb":
            txt_files = list(data_files.glob("*txt"))
            for guid, txt_file in enumerate(txt_files):
                example = parsing.brat_parse_to_bigbio_kb(
                    parsing.parse_brat_file(txt_file)
                )
                example = self._standardize_arguments_roles(example)
                example["id"] = str(guid)
                yield guid, example
        else:
            raise ValueError(f"Invalid config: {self.config.name}")
