import numpy as np
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import f1_score, matthews_corrcoef
from tqdm import auto as tqdm_lib
from . common import HFTask, simple_accuracy_metric, yesno

class OpenBookQA(HFTask):
    DATASET_PATH = "openbookqa"
    DATASET_NAME = "main"

    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):
        if self.has_training_docs():
            if self._training_docs is None:
                self._training_docs = list(self.data["train"])
            return self._training_docs

    def validation_docs(self):
        if self.has_validation_docs():
            return self.data["validation"]

    def test_docs(self):
        if self.has_test_docs():
            return self.data["test"]

    def fewshot_description(self):
        return "Text of the question prompt\nText of the answer completion"

    def doc_to_text(self, doc, include_target=True):
        text = doc['question_stem'] + '\n'
        if include_target:
            letter_answer = doc['answerKey']
            if letter_answer == 'A':
                index = 0
            elif letter_answer == 'B':
                index = 1
            elif letter_answer == 'C':
                index = 2
            elif letter_answer == 'D':
                index = 3
            else:
                raise ValueError("OpenBookQA from HF datasets contained an invalid answer key")
            text += doc['choices']['text'][index] + '.'
        return text

    def evaluate(self, docs, lm, provide_description, num_fewshot):
        # TODO: Write evaluation function
        raise NotImplementedError()