""" Loading script for the Keyword PubMed dataset."""


import os
from pathlib import Path
import re

import datasets


class KeywordPubmedDataset(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.1.0")



    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="sentence", version=VERSION, description="Comprises sentences that contain a keyword"),
        datasets.BuilderConfig(name="document", version=VERSION, description="Contains all the sentences in a document that contains at least a keyword"),
    ]

    DEFAULT_CONFIG_NAME = "document"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        if self.config.name == "sentence":
            features = datasets.Features(
                {
                    "sentence": datasets.Value("string"),
                    "pmcid": datasets.Value("string"),
                    "keyword_rank": datasets.Value("int32"),
                    "keyword_indices": datasets.Sequence(datasets.Value("int32"))
                }
            )
        else:
            features = datasets.Features(
                {
                    "sentence": datasets.Value("string"),
                    "pmcid": datasets.Value("string"),
                    "keyword_rank": datasets.Value("int32"),
                    "keyword_indices": datasets.Sequence(datasets.Value("int32"))
                }
            )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description= "Dataset for MLM comprising sentences that contain a keyword relevant to the domain",
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # 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.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            # homepage=_HOMEPAGE,
            # License for the dataset if available
            # license=_LICENSE,
            # Citation for the dataset
            # citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        

        if self.config.data_dir:
            data_dir = self.config.data_dir
        else:
            data_dir = dl_manager.download_and_extract('data_files.tar.gz')

        # Load the keywords from the file
        with open(os.path.join(data_dir, 'keywords.txt'), 'r') as f:
            keyword_ranks = {line.strip().split(":")[0].lower():rank for rank, line in enumerate(f)}
            keywords = set(keyword_ranks.keys())

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "dirpath": os.path.join(data_dir, "train"),
                    "keywords": keywords,
                    "ranks": keyword_ranks,
                },
            ),

            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "dirpath": os.path.join(data_dir, "dev"),
                    "keywords": keywords,
                    "ranks": keyword_ranks,
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, dirpath, keywords, ranks):
        item_ix = 0
        for filepath in Path(dirpath).iterdir():
            filepath = Path(filepath)
            if filepath.suffix == ".txt":
                pmcid = filepath.name.split(".")[0]
                with filepath.open(encoding="utf-8") as f:
                    for sentence in f:
                        sentence = sentence.strip()
                        if sentence: # Ignore blanks
                            sentence = re.sub("\s+", " ", sentence)
                            kw_indices,  rank = self._keyword_indices(sentence, keywords, ranks)
                            has_keyword  = rank > -1
                            if self.config.name == "sentence":
                                # Yields examples as (key, example) tuples
                                if has_keyword:
                                    yield item_ix, {
                                        "sentence": sentence,
                                        "keyword_rank": rank,
                                        "pmcid": pmcid,
                                        "keyword_indices": kw_indices
                                    }
                                    item_ix += 1

                            else: # Else document
                                yield item_ix, {
                                    "sentence": sentence,
                                    "keyword_rank": rank,
                                    "pmcid": pmcid,
                                    "keyword_indices": kw_indices
                                }
                                item_ix += 1

    def _keyword_indices(self, sentence, keywords, ranks):
        # Lowercase and split the sentence
        words = sentence.lower().split()
        indices = list()
        top_rank = -1
        # Check every word until it finds a keyword
        for w_ix, word in enumerate(words):
            if word in keywords:
                indices.append(w_ix)
                rank = ranks[word]
                if rank < top_rank or top_rank == -1:
                    top_rank = rank
        return indices, top_rank


if __name__  == "__main__":
    ds = KeywordPubmedDataset()
    ds.download_and_prepare()