"""
    SFT dataset to reject toxic questions

"""
import random

from custom_datasets.formatting import QA_SPECIAL_TOKENS, format_pair
from datasets import load_dataset
from torch.utils.data import Dataset


class ProsocialDialogueExplaination(Dataset):
    name = "explain_prosocial"
    TEMPLATE = [
        # 0 : reply or sentence of interest, 1 : reason of caution
        ("'{0}' Why is this sentence {1}", "This sentence is {1} because {0}"),
        ("Explain to me why this sentence is {1}: {0}", "This sentence is {1} because {0}"),
        ("'{0}' Why is this sentence {1}", "This sentence may need {1} because {0}"),
        ("'{0}' Explain to me why we need to {1} of this sentence", "We need to be {1} of this sentence because {0}"),
    ]

    def __init__(self, split="train", cache_dir=".cache") -> None:
        super().__init__()
        dataset = load_dataset("allenai/prosocial-dialog", cache_dir=cache_dir)[split]
        self.pairs = []
        for row in dataset:
            for safety_annotation, safe_answer in zip(row["safety_annotations"], row["safety_annotation_reasons"]):
                (prompt_template, answer_template) = random.choice(self.TEMPLATE)
                self.pairs.append(
                    (
                        prompt_template.format(row["context"], safety_annotation),
                        answer_template.format(safe_answer, safety_annotation),
                    )
                )

    def __len__(self):
        return len(self.pairs)

    def __getitem__(self, idx):
        return format_pair(self.pairs[idx])


class ProsocialDialogue(Dataset):
    name = "prosocial_dialogue"
    """
        ProsocialDialog, we set up a human-AI collaborative data creation framework,
        where GPT-3 generates the potentially unsafe utterances, and crowdworkers
        provide prosocial responses to them. This approach allows us to circumvent
        two substantial challenges:
        (1) there are no available large-scale corpora of multiturn prosocial conversations
            between humans
        (2) asking humans to write unethical, toxic, or problematic utterances could result
            in psychological harms (Roberts, 2017; Steiger et al., 2021).
    """
    PREFIX = "<prefix>You are now a prosocial chatbot, be caution and casual when reply</prefix>"

    def __init__(self, split="train", cache_dir=".cache") -> None:
        super().__init__()
        dataset = load_dataset("allenai/prosocial-dialog", cache_dir=cache_dir)[split]
        self.pairs = []
        for row in dataset:
            prompt = QA_SPECIAL_TOKENS["Question"] + row["context"] + QA_SPECIAL_TOKENS["Answer"]
            for answer in row["rots"]:
                self.pairs.append((self.PREFIX + prompt, answer))

    def __len__(self):
        return len(self.pairs)

    def __getitem__(self, idx):
        return self.pairs[idx]