import json
import os

import datasets
from datasets.tasks import TextClassification

_CITATION = None


_DESCRIPTION = """
 Patent Classification Dataset: a classification of Patents (9 classes).
 It contains 9 unbalanced classes, 35k Patents and summaries divided into 3 splits: train (25k), val (5k) and test (5k).
 Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang 
 See: https://aclanthology.org/P19-1212.pdf 
 See: https://evasharma.github.io/bigpatent/
"""

_LABELS = [
    "Human Necessities",
    "Performing Operations; Transporting",
    "Chemistry; Metallurgy",
    "Textiles; Paper",
    "Fixed Constructions",
    "Mechanical Engineering; Lightning; Heating; Weapons; Blasting",
    "Physics",
    "Electricity",
    "General tagging of new or cross-sectional technology",
]

class PatentClassificationConfig(datasets.BuilderConfig):
    """BuilderConfig for PatentClassification."""

    def __init__(self, **kwargs):
        """BuilderConfig for PatentClassification.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(PatentClassificationConfig, self).__init__(**kwargs)


class PatentClassificationDataset(datasets.GeneratorBasedBuilder):
    """PatentClassification Dataset: classification of Patents (9 classes)."""
    
    _DOWNLOAD_URL = "https://huggingface.co/datasets/ccdv/patent-classification/resolve/main/"
    _TRAIN_FILE = "train_data.txt"
    _VAL_FILE = "val_data.txt"
    _TEST_FILE = "test_data.txt"
    _LABELS_DICT = {label: i for i, label in enumerate(_LABELS)}

    BUILDER_CONFIGS = [
        PatentClassificationConfig(
            name="patent",
            version=datasets.Version("1.0.0"),
            description="Patent Classification Dataset: A classification task of Patents (9 classes)",
        ),

        PatentClassificationConfig(
            name="abstract",
            version=datasets.Version("1.0.0"),
            description="Patent Classification Dataset: A classification task of Patents with abstracts (9 classes)",
        ),
    ]

    DEFAULT_CONFIG_NAME = "patent"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=_LABELS),
                }
            ),
            supervised_keys=None,
            citation=_CITATION,
            task_templates=[TextClassification(
                text_column="text", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        train_path = dl_manager.download_and_extract(self._TRAIN_FILE)
        val_path = dl_manager.download_and_extract(self._VAL_FILE)
        test_path = dl_manager.download_and_extract(self._TEST_FILE)
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
            ),
        ]
    
    def _generate_examples(self, filepath):
        """Generate PatentClassification examples."""
        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                data = json.loads(row)
                label = self._LABELS_DICT[data["label"]]

                if self.config.name == "abstract":
                    text = data["abstract"]
                else:
                    text = data["description"]
                yield id_, {"text": text, "label": label}