import os
from typing import Union, List

from lm_eval.api.task import ConfigurableTask
from lm_eval.api.instance import Instance
# from lm_eval.api.registry import register_task
from lm_eval.api.metrics import mean

from src.backend.envs import DEVICE

import spacy
from selfcheckgpt.modeling_selfcheck import SelfCheckMQAG, SelfCheckNLI, SelfCheckBERTScore, SelfCheckNgram


# @register_task("selfcheckgpt")
class SelfCheckGPT(ConfigurableTask):
    VERSION = 0.0
    DATASET_PATH = "potsawee/wiki_bio_gpt3_hallucination"
    DATASET_NAME = None
    OUTPUT_TYPE = 'generate_until'

    def __init__(self):
        super().__init__(config={'metadata': {'version': self.VERSION}})
        # these end tokens are hard coded because of the current limitaion of the llm-eval.
        self.generation_kwargs = {"until": ["\n\n", "<unk>", "<|im_end|>", "</s>"], "max_length": 512}
        self.generation_kwargs_sampling_number = 5  # the number of sampling for self-consistence
        self.generation_kwargs_sampling = {"temperature": 0.99, "do_sample": True, "until": ["\n\n", "<unk>", "<|im_end|>", "</s>"], "max_length": 512}

        self.selfcheckgpt_type = os.environ.get('SELFCHECKGPTTYPE', 'SelfCheckNLI')
        self.selfcheckgpt_device = os.environ.get('SELFCHECKGPTDEVICE', DEVICE)
        self.selfcheckgpt_nlp = spacy.load("en_core_web_sm")

        if self.selfcheckgpt_type == 'SelfCheckNgram':
            self.selfcheckgpt = SelfCheckNgram(n=1)
        elif self.selfcheckgpt_type == 'SelfCheckBERTScore':
            self.selfcheckgpt = SelfCheckBERTScore(rescale_with_baseline=True)
        elif self.selfcheckgpt_type == 'SelfCheckMQAG':
            self.selfcheckgpt = SelfCheckMQAG(device=self.selfcheckgpt_device)
        elif self.selfcheckgpt_type == 'SelfCheckNLI':
            self.selfcheckgpt = SelfCheckNLI(device=self.selfcheckgpt_device)
        self.SelfCheckNLI_error_cnt = 0

    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["evaluation"]

    def doc_to_text(self, doc):
        doc_text = doc["wiki_bio_text"]
        doc_text = doc_text.split()
        doc_text = " ".join(doc_text[:5])
        # prompt = f"This is a passage from Wikipedia about {context}:\n\n"
        doc_text = f"Please generate a Wikipedia passage starting with: {doc_text}\n"
        return doc_text

    def doc_to_target(self, doc):
        answer = doc['wiki_bio_text']
        return answer

    def construct_requests(self, doc: dict, ctx: str, **kwargs) -> Union[List[Instance], Instance]:
        arguments = (ctx, self.generation_kwargs)
        request_list = [
            Instance(request_type='generate_until', doc=doc, arguments=arguments, idx=0, **kwargs),
        ]
        sampling_arguments = (ctx, self.generation_kwargs_sampling)
        request_list.extend([
            Instance(request_type='generate_until', doc=doc, arguments=sampling_arguments, idx=idx, **kwargs)
            for idx in range(1, self.generation_kwargs_sampling_number+1)
            ]
        )
        return request_list

    def process_results(self, doc, results):
        response_temperature_0 = results[0]
        other_responses = results[1:]
        passage = self.doc_to_target(doc)

        sentences = self.selfcheckgpt_nlp(response_temperature_0)
        sentences = [sent.text.strip() for sent in sentences.sents]
        if self.selfcheckgpt_type == 'SelfCheckNgram':
            selfcheckgpt_scores = self.selfcheckgpt.predict(sentences=sentences, passage=response_temperature_0, sampled_passages=other_responses)
            return {
                'avg-selfcheckgpt': selfcheckgpt_scores['doc_level']['avg_neg_logprob'],
                'max-selfcheckgpt': selfcheckgpt_scores['doc_level']['avg_max_neg_logprob']
            }

        elif self.selfcheckgpt_type == 'SelfCheckBERTScore':
            selfcheckgpt_scores = self.selfcheckgpt.predict(sentences=sentences, sampled_passages=other_responses)
        elif self.selfcheckgpt_type == 'SelfCheckMQAG':
            selfcheckgpt_scores = self.selfcheckgpt.predict(
                sentences=sentences,
                passage=response_temperature_0,
                sampled_passages=other_responses,
                num_questions_per_sent=5,          # number of questions to be drawn
                scoring_method='bayes_with_alpha', # options = 'counting', 'bayes', 'bayes_with_alpha'
                beta1=0.8, beta2=0.8)            # additional params depending on scoring_method
        elif self.selfcheckgpt_type == 'SelfCheckNLI':
            selfcheckgpt_scores = self.selfcheckgpt.predict(sentences=sentences, sampled_passages=other_responses)

            if len(selfcheckgpt_scores) == 0:
                self.SelfCheckNLI_error_cnt += 1
                print(f"SelfCheckNLI Warning.SelfCheckNLI_error_cnt:{self.SelfCheckNLI_error_cnt}. This instance is marked as hallucinated with 0.0.")
                result = {
                    'avg-selfcheckgpt': 0.0,
                    'max-selfcheckgpt': 0.0
                }

            else:
                threshold = 0.6 # https://huggingface.co/blog/dhuynh95/automatic-hallucination-detection
                # passage is hallucianted if one sentence is hallucinated. It's very strict.
                selfcheckgpt_scores_max = 0.0 if max(selfcheckgpt_scores) > threshold else 1.0
                # passage is hallucianted if average score of all sentences is hallucinated.
                selfcheckgpt_scores_avg = 0.0 if sum(selfcheckgpt_scores) / len(selfcheckgpt_scores) > threshold else 1.0
                result = {'avg-selfcheckgpt': selfcheckgpt_scores_avg, 'max-selfcheckgpt': selfcheckgpt_scores_max}
            return result

        selfcheckgpt_scores_avg = sum(selfcheckgpt_scores) / len(selfcheckgpt_scores) if len(selfcheckgpt_scores) > 0 else 0
        selfcheckgpt_scores_max = max(selfcheckgpt_scores)

        return {'avg-selfcheckgpt': selfcheckgpt_scores_avg, 'max-selfcheckgpt': selfcheckgpt_scores_max}

    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 ["avg-selfcheckgpt", "max-selfcheckgpt"]}

    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 ["avg-selfcheckgpt", "max-selfcheckgpt"]}