from collections import defaultdict
from typing import Any, Dict

from evalscope.benchmarks import Benchmark, DataAdapter
from evalscope.constants import EvalType, OutputType
from evalscope.metrics import exact_match
from evalscope.metrics.completion_parsers import ResponseParser

SUBSET_LIST = [
    'computer science', 'math', 'chemistry', 'engineering', 'law', 'biology', 'health', 'physics', 'business',
    'philosophy', 'economics', 'other', 'psychology', 'history'
]


@Benchmark.register(
    name='mmlu_pro',
    pretty_name='MMLU-Pro',
    tags=['MCQ', 'Knowledge'],
    description=
    'MMLU-Pro is a benchmark for evaluating language models on multiple-choice questions across various subjects. It includes questions from different domains, where the model must select the correct answer from given options.',  # noqa: E501
    dataset_id='modelscope/MMLU-Pro',
    model_adapter=OutputType.GENERATION,
    output_types=[OutputType.MULTIPLE_CHOICE, OutputType.GENERATION],
    subset_list=SUBSET_LIST,
    metric_list=['AverageAccuracy'],
    few_shot_num=5,
    train_split='validation',
    eval_split='test',
    prompt_template=
    'The following are multiple choice questions (with answers) about {subset_name}. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n{query}',  # noqa: E501
)
class MMLUProAdapter(DataAdapter):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

        self.choices = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']

    def load(self, **kwargs):
        # default load all data
        kwargs['subset_list'] = ['default']
        data_dict = super().load(**kwargs)
        return self.reformat_subset(data_dict, subset_key='category')

    def gen_prompt(self, input_d: Dict, subset_name: str, few_shot_list: list, **kwargs) -> Any:
        if self.few_shot_num > 0:
            prefix = self.format_fewshot_examples(few_shot_list)
        else:
            prefix = ''
        query = prefix + 'Q: ' + input_d['question'] + '\n' + \
            self.__form_options(input_d['options']) + '\n'

        full_prompt = self.prompt_template.format(subset_name=subset_name, query=query)
        return self.gen_prompt_data(full_prompt)

    def format_fewshot_examples(self, few_shot_list):
        # load few-shot prompts for each category
        prompts = ''
        for index, d in enumerate(few_shot_list):
            prompts += 'Q: ' + d['question'] + '\n' + \
                self.__form_options(d['options']) + '\n' + \
                d['cot_content'] + '\n\n'
        return prompts

    def __form_options(self, options: list):
        option_str = 'Options are:\n'
        for opt, choice in zip(options, self.choices):
            option_str += f'({choice}): {opt}' + '\n'
        return option_str

    def get_gold_answer(self, input_d: dict) -> str:
        """
        Parse the raw input labels (gold).

        Args:
            input_d: input raw data. Depending on the dataset.

        Returns:
            The parsed input. e.g. gold answer ... Depending on the dataset.
        """
        return input_d['answer']

    def parse_pred_result(self, result: str, raw_input_d: dict = None, eval_type: str = EvalType.CHECKPOINT) -> str:
        """
        Parse the predicted result and extract proper answer.

        Args:
            result: Predicted answer from the model. Usually a string for chat.
            raw_input_d: The raw input. Depending on the dataset.
            eval_type: 'checkpoint' or 'service' or `custom`, default: 'checkpoint'

        Returns:
            The parsed answer. Depending on the dataset. Usually a string for chat.
        """
        if self.model_adapter == OutputType.MULTIPLE_CHOICE:
            return result
        else:
            return ResponseParser.parse_first_option(result, options=self.choices)

    def match(self, gold: str, pred: str) -> float:
        """
        Match the gold answer and the predicted answer.

        Args:
            gold (Any): The golden answer. Usually a string for chat/multiple-choice-questions.
                        e.g. 'A', extracted from get_gold_answer method.
            pred (Any): The predicted answer. Usually a string for chat/multiple-choice-questions.
                        e.g. 'B', extracted from parse_pred_result method.

        Returns:
            The match result. Usually a score (float) for chat/multiple-choice-questions.
        """
        return exact_match(gold=gold, pred=pred)