# 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.
# 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.
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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_2013_pc"
_DISPLAYNAME = "BioNLP 2013 PC"

_UNIFIED_VIEW_NAME = "bigbio"

_LANGUAGES = [Lang.EN]
_PUBMED = True
_LOCAL = False
_CITATION = """\
@inproceedings{ohta-etal-2013-overview,
    title = "Overview of the Pathway Curation ({PC}) task of {B}io{NLP} Shared Task 2013",
    author = "Ohta, Tomoko  and
      Pyysalo, Sampo  and
      Rak, Rafal  and
      Rowley, Andrew  and
      Chun, Hong-Woo  and
      Jung, Sung-Jae  and
      Choi, Sung-Pil  and
      Ananiadou, Sophia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the {B}io{NLP} Shared Task 2013 Workshop",
    month = aug,
    year = "2013",
    address = "Sofia, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W13-2009",
    pages = "67--75",
}
"""

_DESCRIPTION = """\
the Pathway Curation (PC) task is a main event extraction task of the BioNLP shared task (ST) 2013.
The PC task concerns the automatic extraction of biomolecular reactions from text.
The task setting, representation and semantics are defined with respect to pathway
model standards and ontologies (SBML, BioPAX, SBO) and documents selected by relevance
to specific model reactions. Two BioNLP ST 2013 participants successfully completed
the PC task. The highest achieved F-score, 52.8%, indicates that event extraction is
a promising approach to supporting pathway curation efforts.
"""

_HOMEPAGE = "https://github.com/openbiocorpora/bionlp-st-2013-pc"

_LICENSE = Licenses.GENIA_PROJECT_LICENSE

_URLs = {
    "bionlp_st_2013_pc": "https://github.com/openbiocorpora/bionlp-st-2013-pc/archive/refs/heads/master.zip",
}

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


class bionlp_st_2013_pc(datasets.GeneratorBasedBuilder):
    """the Pathway Curation (PC) task is a main event extraction task of the BioNLP shared task (ST) 2013."""

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="bionlp_st_2013_pc_source",
            version=SOURCE_VERSION,
            description="bionlp_st_2013 source schema",
            schema="source",
            subset_id="bionlp_st_2013_pc",
        ),
        BigBioConfig(
            name="bionlp_st_2013_pc_bigbio_kb",
            version=BIGBIO_VERSION,
            description="bionlp_st_2013_pc BigBio schema",
            schema="bigbio_kb",
            subset_id="bionlp_st_2013_pc",
        ),
    ]

    DEFAULT_CONFIG_NAME = "bionlp_st_2013_pc_source"

    _ROLE_MAPPING = {
        "Theme2": "Theme",
        "Theme3": "Theme",
        "Theme4": "Theme",
        "Participant2": "Participant",
        "Participant3": "Participant",
        "Participant4": "Participant",
        "Participant5": "Participant",
        "Product2": "Product",
        "Product3": "Product",
        "Product4": "Product",
    }

    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(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            features=features,
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # This is not applicable for MLEE.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=str(_LICENSE),
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:
        my_urls = _URLs[_DATASETNAME]
        data_dir = Path(dl_manager.download_and_extract(my_urls))
        data_files = {
            "train": data_dir / f"bionlp-st-2013-pc-master" / "original-data" / "train",
            "dev": data_dir / f"bionlp-st-2013-pc-master" / "original-data" / "devel",
            "test": data_dir / f"bionlp-st-2013-pc-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}")
