import os
import random
from typing import Dict, List

from pathlib import Path
import numpy as np

import datasets

_DESCRIPTION = """\
ddd
"""

_HOMEPAGE = "ddd"

_LICENSE = "unknown"

_CITATION = r"""\
@inproceedings{grouin-etal-2021-classification,
    title = "Classification de cas cliniques et {\'e}valuation automatique de r{\'e}ponses d{'}{\'e}tudiants : pr{\'e}sentation de la campagne {DEFT} 2021 (Clinical cases classification and automatic evaluation of student answers : Presentation of the {DEFT} 2021 Challenge)",
    author = "Grouin, Cyril  and
      Grabar, Natalia  and
      Illouz, Gabriel",
    booktitle = "Actes de la 28e Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Atelier D{\'E}fi Fouille de Textes (DEFT)",
    month = "6",
    year = "2021",
    address = "Lille, France",
    publisher = "ATALA",
    url = "https://aclanthology.org/2021.jeptalnrecital-deft.1",
    pages = "1--13",
    abstract = "Le d{\'e}fi fouille de textes (DEFT) est une campagne d{'}{\'e}valuation annuelle francophone. Nous pr{\'e}sentons les corpus et baselines {\'e}labor{\'e}es pour trois t{\^a}ches : (i) identifier le profil clinique de patients d{\'e}crits dans des cas cliniques, (ii) {\'e}valuer automatiquement les r{\'e}ponses d{'}{\'e}tudiants sur des questionnaires en ligne (Moodle) {\`a} partir de la correction de l{'}enseignant, et (iii) poursuivre une {\'e}valuation de r{\'e}ponses d{'}{\'e}tudiants {\`a} partir de r{\'e}ponses d{\'e}j{\`a} {\'e}valu{\'e}es par l{'}enseignant. Les r{\'e}sultats varient de 0,394 {\`a} 0,814 de F-mesure sur la premi{\`e}re t{\^a}che (7 {\'e}quipes), de 0,448 {\`a} 0,682 de pr{\'e}cision sur la deuxi{\`e}me (3 {\'e}quipes), et de 0,133 {\`a} 0,510 de pr{\'e}cision sur la derni{\`e}re (3 {\'e}quipes).",
    language = "French",
}
"""

_SPECIALITIES = ['immunitaire', 'endocriniennes', 'blessures', 'chimiques', 'etatsosy', 'nutritionnelles', 'infections', 'virales', 'parasitaires', 'tumeur', 'osteomusculaires', 'stomatognathique', 'digestif', 'respiratoire', 'ORL', 'nerveux', 'oeil', 'homme', 'femme', 'cardiovasculaires', 'hemopathies', 'genetique', 'peau']

_LABELS_BASE = ['anatomie', 'date', 'dose', 'duree', 'examen', 'frequence', 'mode', 'moment', 'pathologie', 'sosy', 'substance', 'traitement', 'valeur']

_URL = "data.zip"


class DEFT2021(datasets.GeneratorBasedBuilder):

    DEFAULT_CONFIG_NAME = "ner"

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="cls", version="1.0.0", description="DEFT 2021 corpora - Classification task"),
        datasets.BuilderConfig(name="ner", version="1.0.0", description="DEFT 2021 corpora - Named-entity recognition task"),
    ]

    def _info(self):

        if self.config.name.find("cls") != -1:

            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document_id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "specialities": datasets.Sequence(
                        datasets.features.ClassLabel(names=_SPECIALITIES),
                    ),
                    "specialities_one_hot": datasets.Sequence(
                        datasets.Value("float"),
                    ),
                }
            )

        elif self.config.name.find("ner") != -1:

            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document_id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                'O', 'B-anatomie', 'I-anatomie', 'B-date', 'I-date', 'B-dose',
                                'I-dose', 'B-duree', 'I-duree', 'B-examen', 'I-examen', 'B-frequence',
                                'I-frequence', 'B-mode', 'I-mode', 'B-moment', 'I-moment',
                                'B-pathologie', 'I-pathologie', 'B-sosy', 'I-sosy', 'B-substance',
                                'I-substance', 'B-traitement', 'I-traitement', 'B-valeur', 'I-valeur'
                            ],
                        )
                    ),
                }
            )

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

    def _split_generators(self, dl_manager):

        data_dir = dl_manager.download_and_extract(_URL).rstrip("/")

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

    def remove_prefix(self, a: str, prefix: str) -> str:
        if a.startswith(prefix):
            a = a[len(prefix):]
        return a

    def parse_brat_file(self, txt_file: Path, annotation_file_suffixes: List[str] = None, parse_notes: bool = False) -> Dict:

        example = {}
        example["document_id"] = txt_file.with_suffix("").name
        with txt_file.open() as f:
            example["text"] = f.read()

        # If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
        # for event extraction
        if annotation_file_suffixes is None:
            annotation_file_suffixes = [".a1", ".a2", ".ann"]

        if len(annotation_file_suffixes) == 0:
            raise AssertionError(
                "At least one suffix for the to-be-read annotation files should be given!"
            )

        ann_lines = []
        for suffix in annotation_file_suffixes:
            annotation_file = txt_file.with_suffix(suffix)
            if annotation_file.exists():
                with annotation_file.open() as f:
                    ann_lines.extend(f.readlines())

        example["text_bound_annotations"] = []
        example["events"] = []
        example["relations"] = []
        example["equivalences"] = []
        example["attributes"] = []
        example["normalizations"] = []

        if parse_notes:
            example["notes"] = []

        for line in ann_lines:
            line = line.strip()
            if not line:
                continue

            if line.startswith("T"):  # Text bound
                ann = {}
                fields = line.split("\t")

                ann["id"] = fields[0]
                ann["type"] = fields[1].split()[0]
                ann["offsets"] = []
                span_str = self.remove_prefix(fields[1], (ann["type"] + " "))
                text = fields[2]
                for span in span_str.split(";"):
                    start, end = span.split()
                    ann["offsets"].append([int(start), int(end)])

                # Heuristically split text of discontiguous entities into chunks
                ann["text"] = []
                if len(ann["offsets"]) > 1:
                    i = 0
                    for start, end in ann["offsets"]:
                        chunk_len = end - start
                        ann["text"].append(text[i:chunk_len + i])
                        i += chunk_len
                        while i < len(text) and text[i] == " ":
                            i += 1
                else:
                    ann["text"] = [text]

                example["text_bound_annotations"].append(ann)

            elif line.startswith("E"):
                ann = {}
                fields = line.split("\t")

                ann["id"] = fields[0]

                ann["type"], ann["trigger"] = fields[1].split()[0].split(":")

                ann["arguments"] = []
                for role_ref_id in fields[1].split()[1:]:
                    argument = {
                        "role": (role_ref_id.split(":"))[0],
                        "ref_id": (role_ref_id.split(":"))[1],
                    }
                    ann["arguments"].append(argument)

                example["events"].append(ann)

            elif line.startswith("R"):
                ann = {}
                fields = line.split("\t")

                ann["id"] = fields[0]
                ann["type"] = fields[1].split()[0]

                ann["head"] = {
                    "role": fields[1].split()[1].split(":")[0],
                    "ref_id": fields[1].split()[1].split(":")[1],
                }
                ann["tail"] = {
                    "role": fields[1].split()[2].split(":")[0],
                    "ref_id": fields[1].split()[2].split(":")[1],
                }

                example["relations"].append(ann)

            # '*' seems to be the legacy way to mark equivalences,
            # but I couldn't find any info on the current way
            # this might have to be adapted dependent on the brat version
            # of the annotation
            elif line.startswith("*"):
                ann = {}
                fields = line.split("\t")

                ann["id"] = fields[0]
                ann["ref_ids"] = fields[1].split()[1:]

                example["equivalences"].append(ann)

            elif line.startswith("A") or line.startswith("M"):
                ann = {}
                fields = line.split("\t")

                ann["id"] = fields[0]

                info = fields[1].split()
                ann["type"] = info[0]
                ann["ref_id"] = info[1]

                if len(info) > 2:
                    ann["value"] = info[2]
                else:
                    ann["value"] = ""

                example["attributes"].append(ann)

            elif line.startswith("N"):
                ann = {}
                fields = line.split("\t")

                ann["id"] = fields[0]
                ann["text"] = fields[2]

                info = fields[1].split()

                ann["type"] = info[0]
                ann["ref_id"] = info[1]
                ann["resource_name"] = info[2].split(":")[0]
                ann["cuid"] = info[2].split(":")[1]
                example["normalizations"].append(ann)

            elif parse_notes and line.startswith("#"):
                ann = {}
                fields = line.split("\t")

                ann["id"] = fields[0]
                ann["text"] = fields[2] if len(fields) == 3 else "<BB_NULL_STR>"

                info = fields[1].split()

                ann["type"] = info[0]
                ann["ref_id"] = info[1]
                example["notes"].append(ann)
        return example

    def _to_source_example(self, brat_example: Dict) -> Dict:

        source_example = {
            "document_id": brat_example["document_id"],
            "text": brat_example["text"],
        }

        source_example["entities"] = []

        for entity_annotation in brat_example["text_bound_annotations"]:
            entity_ann = entity_annotation.copy()

            # Change id property name
            entity_ann["entity_id"] = entity_ann["id"]
            entity_ann.pop("id")

            # Add entity annotation to sample
            source_example["entities"].append(entity_ann)

        return source_example

    def convert_to_prodigy(self, json_object, list_label):

        def prepare_split(text):

            rep_before = ['?', '!', ';', '*']
            rep_after = ['’', "'"]
            rep_both = ['-', '/', '[', ']', ':', ')', '(', ',', '.']

            for i in rep_before:
                text = text.replace(i, ' ' + i)

            for i in rep_after:
                text = text.replace(i, i + ' ')

            for i in rep_both:
                text = text.replace(i, ' ' + i + ' ')

            text_split = text.split()

            punctuations = [',', '.']
            for j in range(0, len(text_split)-1):
                if j - 1 >= 0 and j + 1 <= len(text_split) - 1 and text_split[j-1][-1].isdigit() and text_split[j+1][0].isdigit():
                    if text_split[j] in punctuations:
                        text_split[j-1:j+2] = [''.join(text_split[j-1:j+2])]

            text = ' '.join(text_split)

            return text

        new_json = []

        for ex in [json_object]:

            text = prepare_split(ex['text'])

            tokenized_text = text.split()

            list_spans = []

            for a in ex['entities']:

                for o in range(len(a['offsets'])):

                    text_annot = prepare_split(a['text'][o])

                    offset_start = a['offsets'][o][0]
                    offset_end = a['offsets'][o][1]

                    nb_tokens_annot = len(text_annot.split())

                    txt_offsetstart = prepare_split(ex['text'][:offset_start])

                    nb_tokens_before_annot = len(txt_offsetstart.split())

                    token_start = nb_tokens_before_annot
                    token_end = token_start + nb_tokens_annot - 1

                    if a['type'] in list_label:
                        list_spans.append({
                            'start': offset_start,
                            'end': offset_end,
                            'token_start': token_start,
                            'token_end': token_end,
                            'label': a['type'],
                            'id': a['entity_id'],
                            'text': a['text'][o],
                        })

            res = {
                'id': ex['document_id'],
                'document_id': ex['document_id'],
                'text': ex['text'],
                'tokens': tokenized_text,
                'spans': list_spans
            }

            new_json.append(res)

        return new_json

    def convert_to_hf_format(self, json_object):

        dict_out = []

        for i in json_object:

            # Filter annotations to keep the longest annotated spans when there is nested annotations
            selected_annotations = []

            if 'spans' in i:

                for idx_j, j in enumerate(i['spans']):

                    len_j = int(j['end']) - int(j['start'])
                    range_j = [l for l in range(int(j['start']), int(j['end']), 1)]

                    keep = True

                    for idx_k, k in enumerate(i['spans'][idx_j+1:]):

                        len_k = int(k['end']) - int(k['start'])
                        range_k = [l for l in range(int(k['start']), int(k['end']), 1)]

                        inter = list(set(range_k).intersection(set(range_j)))
                        if len(inter) > 0 and len_j < len_k:
                            keep = False

                    if keep:
                        selected_annotations.append(j)

            # Create list of labels + id to separate different annotation and prepare IOB2 format
            nb_tokens = len(i['tokens'])
            ner_tags = ['O'] * nb_tokens

            for slct in selected_annotations:

                for x in range(slct['token_start'], slct['token_end'] + 1, 1):

                    if i['tokens'][x] not in slct['text']:
                        if ner_tags[x-1] == 'O':
                            ner_tags[x-1] = slct['label'] + '-' + slct['id']
                    else:
                        if ner_tags[x] == 'O':
                            ner_tags[x] = slct['label'] + '-' + slct['id']

            # Make IOB2 format
            ner_tags_IOB2 = []
            for idx_l, label in enumerate(ner_tags):

                if label == 'O':
                    ner_tags_IOB2.append('O')
                else:
                    current_label = label.split('-')[0]
                    current_id = label.split('-')[1]
                    if idx_l == 0:
                        ner_tags_IOB2.append('B-' + current_label)
                    elif current_label in ner_tags[idx_l-1]:
                        if current_id == ner_tags[idx_l-1].split('-')[1]:
                            ner_tags_IOB2.append('I-' + current_label)
                        else:
                            ner_tags_IOB2.append('B-' + current_label)
                    else:
                        ner_tags_IOB2.append('B-' + current_label)

            dict_out.append({
                'id': i['id'],
                'document_id': i['document_id'],
                "ner_tags": ner_tags_IOB2,
                "tokens": i['tokens'],
            })

        return dict_out

    def split_sentences(self, json_o):
        """
            Split each document in sentences to fit the 512 maximum tokens of BERT.
        """

        final_json = []

        for i in json_o:

            ind_punc = [index for index, value in enumerate(i['tokens']) if value == '.'] + [len(i['tokens'])]

            for index, value in enumerate(ind_punc):

                if index == 0:
                    final_json.append({
                        'id': i['id'] + '_' + str(index),
                        'document_id': i['document_id'],
                        'ner_tags': i['ner_tags'][:value+1],
                        'tokens': i['tokens'][:value+1]
                    })
                else:
                    prev_value = ind_punc[index-1]
                    final_json.append({
                        'id': i['id'] + '_' + str(index),
                        'document_id': i['document_id'],
                        'ner_tags': i['ner_tags'][prev_value+1:value+1],
                        'tokens': i['tokens'][prev_value+1:value+1]
                    })

        return final_json

    def _generate_examples(self, data_dir, split):

        if self.config.name.find("cls") != -1:

            all_res = {}

            key = 0

            if split == 'train' or split == 'validation':
                split_eval = 'train'
            else:
                split_eval = 'test'

            path_labels = Path(data_dir) / 'evaluations' / f"ref-{split_eval}-deft2021.txt"

            with open(os.path.join(data_dir, 'distribution-corpus.txt')) as f_dist:

                doc_specialities_ = {}

                with open(path_labels) as f_spec:

                    doc_specialities = [line.strip() for line in f_spec.readlines()]

                    for raw in doc_specialities:

                        raw_split = raw.split('\t')

                        if len(raw_split) == 3 and raw_split[0] in doc_specialities_:
                            doc_specialities_[raw_split[0]].append(raw_split[1])

                        elif len(raw_split) == 3 and raw_split[0] not in doc_specialities_:
                            doc_specialities_[raw_split[0]] = [raw_split[1]]

                ann_path = Path(data_dir) / "DEFT-cas-cliniques"

                for guid, txt_file in enumerate(sorted(ann_path.glob("*.txt"))):

                    ann_file = txt_file.with_suffix("").name.split('.')[0] + '.ann'

                    if ann_file in doc_specialities_:

                        res = {}
                        res['document_id'] = txt_file.with_suffix("").name
                        with txt_file.open() as f:
                            res["text"] = f.read()

                        specialities = doc_specialities_[ann_file]

                        # Empty one hot vector
                        one_hot = [0.0 for i in _SPECIALITIES]

                        # Fill up the one hot vector
                        for s in specialities:
                            one_hot[_SPECIALITIES.index(s)] = 1.0

                        all_res[res['document_id']] = {
                            "id": str(key),
                            "document_id": res['document_id'],
                            "text": res["text"],
                            "specialities": specialities,
                            "specialities_one_hot": one_hot,
                        }

                        key += 1

                distribution = [line.strip() for line in f_dist.readlines()]

                random.seed(4)
                train = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'train 2021']
                random.shuffle(train)
                random.shuffle(train)
                random.shuffle(train)
                train, validation = np.split(train, [int(len(train)*0.7096)])

                test = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'test 2021']

                if split == "train":
                    allowed_ids = list(train)
                elif split == "test":
                    allowed_ids = list(test)
                elif split == "validation":
                    allowed_ids = list(validation)

                for r in all_res.values():
                    if r["document_id"] + '.txt' in allowed_ids:
                        yield r["id"], r

        elif self.config.name.find("ner") != -1:

            all_res = []

            key = 0

            with open(os.path.join(data_dir, 'distribution-corpus.txt')) as f_dist:

                distribution = [line.strip() for line in f_dist.readlines()]

                random.seed(4)
                train = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'train 2021']
                random.shuffle(train)
                random.shuffle(train)
                random.shuffle(train)
                train, validation = np.split(train, [int(len(train)*0.73)])
                test = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'test 2021']

                ann_path = Path(data_dir) / "DEFT-cas-cliniques"

                for guid, txt_file in enumerate(sorted(ann_path.glob("*.txt"))):

                    brat_example = self.parse_brat_file(txt_file, parse_notes=True)

                    source_example = self._to_source_example(brat_example)

                    prod_format = self.convert_to_prodigy(source_example, _LABELS_BASE)

                    hf_format = self.convert_to_hf_format(prod_format)

                    hf_split = self.split_sentences(hf_format)

                    for h in hf_split:

                        if len(h['tokens']) > 0 and len(h['ner_tags']) > 0:

                            all_res.append({
                                "id": str(key),
                                "document_id": h['document_id'],
                                "tokens": h['tokens'],
                                "ner_tags": h['ner_tags'],
                            })

                            key += 1

                if split == "train":
                    allowed_ids = list(train)
                elif split == "validation":
                    allowed_ids = list(validation)
                elif split == "test":
                    allowed_ids = list(test)

                for r in all_res:
                    if r["document_id"] + '.txt' in allowed_ids:
                        yield r["id"], r