from lm_eval.api.task import Task
from lm_eval.api.instance import Instance
from lm_eval.api.registry import register_task
from lm_eval.api.metrics import mean
import datasets
from src.backend.tasks.cnndm import utils


@register_task("cnndm")
class CnnDm(Task):
    VERSION = 0
    DATASET_PATH = "cnn_dailymail"
    DATASET_NAME = "3.0.0"

    def __init__(self, data_dir=None, cache_dir=None, download_mode=None, config=None):
        super().__init__(data_dir=data_dir, cache_dir=cache_dir, download_mode=download_mode, config=config)
        print('XXX CNNDM!')

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return True

    def training_docs(self):
        return self.dataset["train"]

    def validation_docs(self):
        return self.dataset["validation"]

    def test_docs(self):
        return self.dataset["test"]

    def doc_to_text(self, doc):
        return f'Document: {doc["article"]}\nSummary:'

    @staticmethod
    def should_decontaminate():
        return True

    def doc_to_decontamination_query(self, doc):
        return doc["article"]

    def doc_to_target(self, doc):
        return doc["highlights"]

    def construct_requests(self, doc, ctx, **kwargs):
        """Uses RequestFactory to construct Requests and returns an iterable of
        Requests which will be sent to the LM.

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param ctx: str
            The context string, generated by fewshot_context. This includes the natural
            language description, as well as the few shot examples, and the question
            part of the document for `doc`.
        """

        return [
            Instance(
                request_type="generate_until",
                doc=doc,
                arguments=(ctx, {"until": ["\n", "."]}),
                idx=0,
                **kwargs
            )
        ]

    def process_results(self, doc, results):
        return utils.process_results(doc, results)

    def aggregation(self):
        """
        :returns: {str: [float] -> float}
            A dictionary where keys are the names of submetrics and values are
            functions that aggregate a list of metrics
        """
        return {k: mean for k in ["rouge1", "rouge2", "rougeL"]}

    def higher_is_better(self):
        """
        :returns: {str: bool}
            A dictionary where keys are the names of submetrics and values are
            whether a higher value of the submetric is better
        """
        return {k: True for k in ["rouge1", "rouge2", "rougeL"]}