import datasets
from datasets.features import ClassLabel, Features, Image
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from datasets import TaskTemplate, ImageClassification

_DESCRIPTION = "none"
_NAMES = ["non-sarcastic", "sarcastic"]
_HOMEPAGE = "https://github.com/headacheboy/data-of-multimodal-sarcasm-detection"
_CITATION = "none"
_LICENSE = "none"
_BASE_URL = "data/images.tar"
_METADATA_URLS = {"train": "data/text/train.txt",
                  "test": "data/text/test2.txt",
                  "valid": "data/text/valid2.txt"}
_IMAGES_DIR = "images/"


@dataclass(frozen=True)
class ImageTextClassification(TaskTemplate):
    task: str = field(default="image-text-classification", metadata={"include_in_asdict_even_if_is_default": True})
    input_schema: ClassVar[Features] = Features({"image": Image()})
    text_schema: ClassVar[Features] = Features({"text": datasets.Value("string")})
    label_schema: ClassVar[Features] = Features({"labels": ClassLabel})
    image_column: str = "image"
    text_column: str = "text"
    label_column: str = "labels"

    def align_with_features(self, features):
        if self.label_column not in features:
            raise ValueError(f"Column {self.label_column} is not present in features.")
        if not isinstance(features[self.label_column], ClassLabel):
            raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
        task_template = copy.deepcopy(self)
        label_schema = self.label_schema.copy()
        label_schema["labels"] = features[self.label_column]
        task_template.__dict__["label_schema"] = label_schema
        return task_template

    @property
    def column_mapping(self) -> Dict[str, str]:
        return {
            self.image_column: "image",
            self.text_column: "text",
            self.label_column: "labels",
        }


class MultimodalSarcasmDetection(datasets.GeneratorBasedBuilder):
    """MultimodalSarcasmDetection Images dataset"""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "text": datasets.Value("string"),
                    "label": datasets.ClassLabel(names=_NAMES),
                }
            ),
            supervised_keys=(("image", "text"), "label"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
            task_templates=[ImageTextClassification(image_column="image", text_column="text", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        archive_path = dl_manager.download(_BASE_URL)
        split_metadata_paths = dl_manager.download(_METADATA_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path),
                    "metadata_path": split_metadata_paths["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path),
                    "metadata_path": split_metadata_paths["test"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path),
                    "metadata_path": split_metadata_paths["valid"],
                },
            ),
        ]

    def _generate_examples(self, images, metadata_path):
        """Generate images and labels for splits."""
        lines = {}
        files_to_keep = set()
        with open(metadata_path, encoding="utf-8") as f:
            for line in f:
                line = line.strip()
                if line:
                    line = eval(line)
                    lines[line[0]] = line[1:]
                    files_to_keep.add(line[0])
        for file_path, file_obj in images:
            if file_path.startswith(_IMAGES_DIR):
                image_id = file_path[len(_IMAGES_DIR): -len(".jpg")]
                if image_id in files_to_keep:
                    line = lines[image_id]
                    yield file_path, {
                        "image": {"path": file_path, "bytes": file_obj.read()},
                        "text": line[0],
                        "label": line[-1]
                    }