# coding=utf-8
# Copyright 2022 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, Tuple

import datasets
import pandas as pd

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

_LANGUAGES = [Lang.ES]
_PUBMED = False
_LOCAL = False
_CITATION = """\
@article{miranda2022overview,

title={Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases
    from clinical texts: results, methods, evaluation and multilingual resources},

author={Miranda-Escalada, Antonio and Gascó, Luis and Lima-López, Salvador and Farré-Maduell,
    Eulàlia and Estrada, Darryl and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras,
    Georgios and Paliouras, Georgios and Krallinger, Martin},

booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum.
    CEUR Workshop Proceedings},

year={2022}
}
"""

_DATASETNAME = "distemist"
_DISPLAYNAME = "DisTEMIST"

_DESCRIPTION = """\
The DisTEMIST corpus is a collection of 1000 clinical cases with disease annotations linked with Snomed-CT concepts.
All documents are released in the context of the BioASQ DisTEMIST track for CLEF 2022.
"""

_HOMEPAGE = "https://zenodo.org/record/6671292"

_LICENSE = Licenses.CC_BY_4p0

_URLS = {
    _DATASETNAME: "https://zenodo.org/record/6671292/files/distemist.zip?download=1",
}

_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]

_SOURCE_VERSION = "5.1.0"
_BIGBIO_VERSION = "1.0.0"


class DistemistDataset(datasets.GeneratorBasedBuilder):
    """
    The DisTEMIST corpus is a collection of 1000 clinical cases with disease annotations linked with Snomed-CT
    concepts.
    """

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="distemist_entities_source",
            version=SOURCE_VERSION,
            description="DisTEMIST (subtrack 1: entities) source schema",
            schema="source",
            subset_id="distemist_entities",
        ),
        BigBioConfig(
            name="distemist_linking_source",
            version=SOURCE_VERSION,
            description="DisTEMIST (subtrack 2: linking) source schema",
            schema="source",
            subset_id="distemist_linking",
        ),
        BigBioConfig(
            name="distemist_entities_bigbio_kb",
            version=BIGBIO_VERSION,
            description="DisTEMIST (subtrack 1: entities) BigBio schema",
            schema="bigbio_kb",
            subset_id="distemist_entities",
        ),
        BigBioConfig(
            name="distemist_linking_bigbio_kb",
            version=BIGBIO_VERSION,
            description="DisTEMIST (subtrack 2: linking) BigBio schema",
            schema="bigbio_kb",
            subset_id="distemist_linking",
        ),
    ]

    DEFAULT_CONFIG_NAME = "distemist_entities_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document_id": datasets.Value("string"),
                    "passages": [
                        {
                            "id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            "text": datasets.Sequence(datasets.Value("string")),
                            "offsets": datasets.Sequence([datasets.Value("int32")]),
                        }
                    ],
                    "entities": [
                        {
                            "id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            "text": datasets.Sequence(datasets.Value("string")),
                            "offsets": datasets.Sequence([datasets.Value("int32")]),
                            "concept_codes": datasets.Sequence(datasets.Value("string")),
                            "semantic_relations": datasets.Sequence(datasets.Value("string")),
                        }
                    ],
                }
            )
        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) -> List[datasets.SplitGenerator]:
        """Returns SplitGenerators."""
        urls = _URLS[_DATASETNAME]
        data_dir = dl_manager.download_and_extract(urls)
        base_bath = Path(data_dir) / "distemist" / "training"
        if self.config.subset_id == "distemist_entities":
            entity_mapping_files = [base_bath / "subtrack1_entities" / "distemist_subtrack1_training_mentions.tsv"]
        else:
            entity_mapping_files = [
                base_bath / "subtrack2_linking" / "distemist_subtrack2_training1_linking.tsv",
                base_bath / "subtrack2_linking" / "distemist_subtrack2_training2_linking.tsv",
            ]
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "entity_mapping_files": entity_mapping_files,
                    "text_files_dir": base_bath / "text_files",
                },
            ),
        ]

    def _generate_examples(
        self,
        entity_mapping_files: List[Path],
        text_files_dir: Path,
    ) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        entities_mapping = pd.concat([pd.read_csv(file, sep="\t") for file in entity_mapping_files])

        entity_file_names = entities_mapping["filename"].unique()

        for uid, filename in enumerate(entity_file_names):
            text_file = text_files_dir / f"{filename}.txt"

            doc_text = text_file.read_text()
            # doc_text = doc_text.replace("\n", "")

            entities_df: pd.DataFrame = entities_mapping[entities_mapping["filename"] == filename]

            example = {
                "id": f"{uid}",
                "document_id": filename,
                "passages": [
                    {
                        "id": f"{uid}_{filename}_passage",
                        "type": "clinical_case",
                        "text": [doc_text],
                        "offsets": [[0, len(doc_text)]],
                    }
                ],
            }
            if self.config.schema == "bigbio_kb":
                example["events"] = []
                example["coreferences"] = []
                example["relations"] = []

            entities = []
            for row in entities_df.itertuples(name="Entity"):
                entity = {
                    "id": f"{uid}_{row.filename}_{row.Index}_entity_id_{row.mark}",
                    "type": row.label,
                    "text": [row.span],
                    "offsets": [[row.off0, row.off1]],
                }
                if self.config.schema == "source":
                    entity["concept_codes"] = []
                    entity["semantic_relations"] = []
                    if self.config.subset_id == "distemist_linking":
                        entity["concept_codes"] = row.code.split("+")
                        entity["semantic_relations"] = row.semantic_rel.split("+")

                elif self.config.schema == "bigbio_kb":
                    if self.config.subset_id == "distemist_linking":
                        entity["normalized"] = [
                            {"db_id": code, "db_name": "SNOMED_CT"} for code in row.code.split("+")
                        ]
                    else:
                        entity["normalized"] = []

                entities.append(entity)

            example["entities"] = entities
            yield uid, example
