import re
from typing import List

import numpy as np

from lm_eval.api.instance import Instance
from lm_eval.api.task import ConfigurableTask


class FDA(ConfigurableTask):
    VERSION = 0
    DATASET_PATH = "hazyresearch/based-fda"
    DATASET_NAME = "default"

    def __init__(self, **kwargs):
        super().__init__(config={"metadata": {"version": self.VERSION}})

    def has_training_docs(self):
        return False

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return False

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

    def doc_to_text(self, doc):
        return doc["text"]

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

    def construct_requests(
        self, doc, ctx, chat_template=None, apply_chat_template=False, **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"], "max_gen_toks": 48}),
                idx=0,
                **kwargs,
            )
        ]

    def process_results(self, doc, results):
        """Take a single document and the LM results and evaluates, returning a
        dict where keys are the names of submetrics and values are the values of
        the metric for that one document

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param results:
            The results of the requests created in construct_requests.
        """
        # continuation, (logprob_unanswerable, _) = results
        continuation = results

        return {"contains": contains_score(continuation[0], [doc["value"]])}

    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 {
            "contains": np.mean,  # Exact match (the normalized answer exactly match the gold answer)
        }

    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 {
            "contains": True,  # Exact match (the normalized answer exactly match the gold answer
        }


def contains_score(prediction: str, labels: List[str]):
    return max(
        int(bool(re.search(re.compile(re.escape(label), re.IGNORECASE), prediction)))
        for label in labels
    )
