import os

import datasets
import numpy as np
import pandas as pd

from .bigbiohub import text_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks

_LANGUAGES = ['French']
_PUBMED = False
_LOCAL = True
_CITATION = """\
 @misc{dalloux, title={Datasets – Clément Dalloux}, url={http://clementdalloux.fr/?page_id=28}, journal={Clément Dalloux}, author={Dalloux, Clément}} 
"""

_DATASETNAME = "essai"
_DISPLAYNAME = "ESSAI"

_DESCRIPTION = """\
We manually annotated two corpora from the biomedical field. The ESSAI corpus \
contains clinical trial protocols in French. They were mainly obtained from the \
National Cancer Institute The typical protocol consists of two parts: the \
summary of the trial, which indicates the purpose of the trial and the methods \
applied; and a detailed description of the trial with the inclusion and \
exclusion criteria. The CAS corpus contains clinical cases published in \
scientific literature and training material. They are published in different \
journals from French-speaking countries (France, Belgium, Switzerland, Canada, \
African countries, tropical countries) and are related to various medical \
specialties (cardiology, urology, oncology, obstetrics, pulmonology, \
gastro-enterology). The purpose of clinical cases is to describe clinical \
situations of patients. Hence, their content is close to the content of clinical \
narratives (description of diagnoses, treatments or procedures, evolution, \
family history, expected audience, etc.). In clinical cases, the negation is \
frequently used for describing the patient signs, symptoms, and diagnosis. \
Speculation is present as well but less frequently.

This version only contain the annotated ESSAI corpus
"""

_HOMEPAGE = "https://clementdalloux.fr/?page_id=28"

_LICENSE = 'Data User Agreement'

_URLS = {
    "essai_source": "",
    "essai_bigbio_text": "",
    "essai_bigbio_kb": "",
}

_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"

_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]


class ESSAI(datasets.GeneratorBasedBuilder):
    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    DEFAULT_CONFIG_NAME = "essai_source"

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="essai_source",
            version=SOURCE_VERSION,
            description="ESSAI source schema",
            schema="source",
            subset_id="essai",
        ),
        BigBioConfig(
            name="essai_bigbio_text",
            version=BIGBIO_VERSION,
            description="ESSAI simplified BigBio schema for negation/speculation classification",
            schema="bigbio_text",
            subset_id="essai",
        ),
        BigBioConfig(
            name="essai_bigbio_kb",
            version=BIGBIO_VERSION,
            description="ESSAI simplified BigBio schema for part-of-speech-tagging",
            schema="bigbio_kb",
            subset_id="essai",
        ),
    ]

    def _info(self):
        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document_id": datasets.Value("string"),
                    "text": [datasets.Value("string")],
                    "lemmas": [datasets.Value("string")],
                    "POS_tags": [datasets.Value("string")],
                    "labels": [datasets.Value("string")],
                }
            )
        elif self.config.schema == "bigbio_text":
            features = text_features
        elif self.config.schema == "bigbio_kb":
            features = kb_features

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=str(_LICENSE),
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        if self.config.data_dir is None:
            raise ValueError(
                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
            )
        else:
            data_dir = self.config.data_dir
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"datadir": data_dir},
            ),
        ]

    def _generate_examples(self, datadir):
        key = 0
        for file in ["ESSAI_neg.txt", "ESSAI_spec.txt"]:
            filepath = os.path.join(datadir, file)
            label = "negation" if "neg" in file else "speculation"
            id_docs = []
            id_words = []
            words = []
            lemmas = []
            POS_tags = []

            with open(filepath) as f:
                for line in f.readlines():
                    line_content = line.split("\t")
                    if len(line_content) > 1:
                        id_docs.append(line_content[0])
                        id_words.append(line_content[1])
                        words.append(line_content[2])
                        lemmas.append(line_content[3])
                        POS_tags.append(line_content[4])

            dic = {
                "id_docs": np.array(list(map(int, id_docs))),
                "id_words": id_words,
                "words": words,
                "lemmas": lemmas,
                "POS_tags": POS_tags,
            }
            if self.config.schema == "source":
                for doc_id in set(dic["id_docs"]):
                    idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
                    text = [dic["words"][id] for id in idces]
                    text_lemmas = [dic["lemmas"][id] for id in idces]
                    POS_tags_ = [dic["POS_tags"][id] for id in idces]
                    yield key, {
                        "id": key,
                        "document_id": doc_id,
                        "text": text,
                        "lemmas": text_lemmas,
                        "POS_tags": POS_tags_,
                        "labels": [label],
                    }
                    key += 1
            elif self.config.schema == "bigbio_text":
                for doc_id in set(dic["id_docs"]):
                    idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
                    text = " ".join([dic["words"][id] for id in idces])
                    yield key, {
                        "id": key,
                        "document_id": doc_id,
                        "text": text,
                        "labels": [label],
                    }
                    key += 1
            elif self.config.schema == "bigbio_kb":
                for doc_id in set(dic["id_docs"]):
                    idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
                    text = [dic["words"][id] for id in idces]
                    POS_tags_ = [dic["POS_tags"][id] for id in idces]

                    data = {
                        "id": str(key),
                        "document_id": doc_id,
                        "passages": [],
                        "entities": [],
                        "relations": [],
                        "events": [],
                        "coreferences": [],
                    }
                    key += 1

                    data["passages"] = [
                        {
                            "id": str(key + i),
                            "type": "sentence",
                            "text": [text[i]],
                            "offsets": [[i, i + 1]],
                        }
                        for i in range(len(text))
                    ]
                    key += len(text)

                    for i in range(len(text)):
                        entity = {
                            "id": key,
                            "type": "POS_tag",
                            "text": [POS_tags_[i]],
                            "offsets": [[i, i + 1]],
                            "normalized": [],
                        }
                        data["entities"].append(entity)
                        key += 1

                    yield key, data