from typing import Dict


def get_task_def_by_task_name_and_type(task_name: str, task_type: str) -> str:
    if task_type in ['STS']:
        return "Retrieve semantically similar text."

    if task_type in ['Summarization']:
        return "Given a news summary, retrieve other semantically similar summaries."

    if task_type in ['BitextMining']:
        return "Retrieve parallel sentences."

    if task_type in ['Classification']:
        task_name_to_instruct: Dict[str, str] = {
            'AmazonCounterfactualClassification': 'Classify a given Amazon customer review text as either counterfactual or not-counterfactual.',
            'AmazonPolarityClassification': 'Classify Amazon reviews into positive or negative sentiment.',
            'AmazonReviewsClassification': 'Classify the given Amazon review into its appropriate rating category.',
            'Banking77Classification': 'Given a online banking query, find the corresponding intents.',
            'EmotionClassification': 'Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise.',
            'ImdbClassification': 'Classify the sentiment expressed in the given movie review text from the IMDB dataset.',
            'MassiveIntentClassification': 'Given a user utterance as query, find the user intents.',
            'MassiveScenarioClassification': 'Given a user utterance as query, find the user scenarios.',
            'MTOPDomainClassification': 'Classify the intent domain of the given utterance in task-oriented conversation.',
            'MTOPIntentClassification': 'Classify the intent of the given utterance in task-oriented conversation.',
            'ToxicConversationsClassification': 'Classify the given comments as either toxic or not toxic.',
            'TweetSentimentExtractionClassification': 'Classify the sentiment of a given tweet as either positive, negative, or neutral.',
            # C-MTEB eval instructions
            'TNews': 'Classify the fine-grained category of the given news title.',
            'IFlyTek': 'Given an App description text, find the appropriate fine-grained category.',
            'MultilingualSentiment': 'Classify sentiment of the customer review into positive, neutral, or negative.',
            'JDReview': 'Classify the customer review for iPhone on e-commerce platform into positive or negative.',
            'OnlineShopping': 'Classify the customer review for online shopping into positive or negative.',
            'Waimai': 'Classify the customer review from a food takeaway platform into positive or negative.',
        }
        return task_name_to_instruct[task_name]

    if task_type in ['Clustering']:
        task_name_to_instruct: Dict[str, str] = {
            'ArxivClusteringP2P': 'Identify the main and secondary category of Arxiv papers based on the titles and abstracts.',
            'ArxivClusteringS2S': 'Identify the main and secondary category of Arxiv papers based on the titles.',
            'BiorxivClusteringP2P': 'Identify the main category of Biorxiv papers based on the titles and abstracts.',
            'BiorxivClusteringS2S': 'Identify the main category of Biorxiv papers based on the titles.',
            'MedrxivClusteringP2P': 'Identify the main category of Medrxiv papers based on the titles and abstracts.',
            'MedrxivClusteringS2S': 'Identify the main category of Medrxiv papers based on the titles.',
            'RedditClustering': 'Identify the topic or theme of Reddit posts based on the titles.',
            'RedditClusteringP2P': 'Identify the topic or theme of Reddit posts based on the titles and posts.',
            'StackExchangeClustering': 'Identify the topic or theme of StackExchange posts based on the titles.',
            'StackExchangeClusteringP2P': 'Identify the topic or theme of StackExchange posts based on the given paragraphs.',
            'TwentyNewsgroupsClustering': 'Identify the topic or theme of the given news articles.',
            # C-MTEB eval instructions
            'CLSClusteringS2S': 'Identify the main category of scholar papers based on the titles.',
            'CLSClusteringP2P': 'Identify the main category of scholar papers based on the titles and abstracts.',
            'ThuNewsClusteringS2S': 'Identify the topic or theme of the given news articles based on the titles.',
            'ThuNewsClusteringP2P': 'Identify the topic or theme of the given news articles based on the titles and contents.',
        }
        return task_name_to_instruct[task_name]

    if task_type in ['Reranking', 'PairClassification']:
        task_name_to_instruct: Dict[str, str] = {
            'AskUbuntuDupQuestions': 'Retrieve duplicate questions from AskUbuntu forum.',
            'MindSmallReranking': 'Retrieve relevant news articles based on user browsing history.',
            'SciDocsRR': 'Given a title of a scientific paper, retrieve the titles of other relevant papers.',
            'StackOverflowDupQuestions': 'Retrieve duplicate questions from StackOverflow forum.',
            'SprintDuplicateQuestions': 'Retrieve duplicate questions from Sprint forum.',
            'TwitterSemEval2015': 'Retrieve tweets that are semantically similar to the given tweet.',
            'TwitterURLCorpus': 'Retrieve tweets that are semantically similar to the given tweet.',
            # C-MTEB eval instructions
            'T2Reranking': 'Given a Chinese search query, retrieve web passages that answer the question.',
            'MMarcoReranking': 'Given a Chinese search query, retrieve web passages that answer the question.',
            'CMedQAv1': 'Given a Chinese community medical question, retrieve replies that best answer the question.',
            'CMedQAv2': 'Given a Chinese community medical question, retrieve replies that best answer the question.',
            'Ocnli': 'Retrieve semantically similar text.',
            'Cmnli': 'Retrieve semantically similar text.',
        }
        return task_name_to_instruct[task_name]

    if task_type in ['Retrieval']:
        if task_name.lower().startswith('cqadupstack'):
            return 'Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question.'

        task_name_to_instruct: Dict[str, str] = {
            'ArguAna': 'Given a claim, find documents that refute the claim.',
            'ClimateFEVER': 'Given a claim about climate change, retrieve documents that support or refute the claim.',
            'DBPedia': 'Given a query, retrieve relevant entity descriptions from DBPedia.',
            'FEVER': 'Given a claim, retrieve documents that support or refute the claim.',
            'FiQA2018': 'Given a financial question, retrieve user replies that best answer the question.',
            'HotpotQA': 'Given a multi-hop question, retrieve documents that can help answer the question.',
            'MSMARCO': 'Given a web search query, retrieve relevant passages that answer the query.',
            'NFCorpus': 'Given a question, retrieve relevant documents that best answer the question.',
            'NQ': 'Given a question, retrieve Wikipedia passages that answer the question.',
            'QuoraRetrieval': 'Given a question, retrieve questions that are semantically equivalent to the given question.',
            'SCIDOCS': 'Given a scientific paper title, retrieve paper abstracts that are cited by the given paper.',
            'SciFact': 'Given a scientific claim, retrieve documents that support or refute the claim.',
            'Touche2020': 'Given a question, retrieve detailed and persuasive arguments that answer the question.',
            'TRECCOVID': 'Given a query on COVID-19, retrieve documents that answer the query.',
            # C-MTEB eval instructions
            'T2Retrieval': 'Given a Chinese search query, retrieve web passages that answer the question.',
            'MMarcoRetrieval': 'Given a web search query, retrieve relevant passages that answer the query.',
            'DuRetrieval': 'Given a Chinese search query, retrieve web passages that answer the question.',
            'CovidRetrieval': 'Given a question on COVID-19, retrieve news articles that answer the question.',
            'CmedqaRetrieval': 'Given a Chinese community medical question, retrieve replies that best answer the question.',
            'EcomRetrieval': 'Given a user query from an e-commerce website, retrieve description sentences of relevant products.',
            'MedicalRetrieval': 'Given a medical question, retrieve user replies that best answer the question.',
            'VideoRetrieval': 'Given a video search query, retrieve the titles of relevant videos.',
        }

        # add lower case keys to match some beir names
        task_name_to_instruct.update({k.lower(): v for k, v in task_name_to_instruct.items()})
        # other cases where lower case match still doesn't work
        task_name_to_instruct['trec-covid'] = task_name_to_instruct['TRECCOVID']
        task_name_to_instruct['climate-fever'] = task_name_to_instruct['ClimateFEVER']
        task_name_to_instruct['dbpedia-entity'] = task_name_to_instruct['DBPedia']
        task_name_to_instruct['webis-touche2020'] = task_name_to_instruct['Touche2020']
        task_name_to_instruct['fiqa'] = task_name_to_instruct['FiQA2018']
        task_name_to_instruct['quora'] = task_name_to_instruct['QuoraRetrieval']

        # for miracl evaluation
        task_name_to_instruct['miracl'] = 'Given a question, retrieve Wikipedia passages that answer the question.'

        return task_name_to_instruct[task_name]

    raise ValueError(f"No instruction config for task {task_name} with type {task_type}")


tasks_desc = {
    'Retrieval': [
        'ArguAna',
        'ClimateFEVER',
        'DBPedia',
        'FEVER',
        'FiQA2018',
        'HotpotQA',
        'MSMARCO',
        'NFCorpus',
        'NQ',
        'QuoraRetrieval',
        'SCIDOCS',
        'SciFact',
        'Touche2020',
        'TRECCOVID',
        'CQADupstackAndroidRetrieval',
        'CQADupstackEnglishRetrieval',
        'CQADupstackGamingRetrieval',
        'CQADupstackGisRetrieval',
        'CQADupstackMathematicaRetrieval',
        'CQADupstackPhysicsRetrieval',
        'CQADupstackProgrammersRetrieval',
        'CQADupstackStatsRetrieval',
        'CQADupstackTexRetrieval',
        'CQADupstackUnixRetrieval',
        'CQADupstackWebmastersRetrieval',
        'CQADupstackWordpressRetrieval'
    ],
    'Classification': [
        # 12
        'AmazonCounterfactualClassification',
        'AmazonPolarityClassification',
        'AmazonReviewsClassification',
        'Banking77Classification',
        'EmotionClassification',
        'ImdbClassification',
        'MassiveIntentClassification',
        'MassiveScenarioClassification',
        'MTOPDomainClassification',
        'MTOPIntentClassification',
        'ToxicConversationsClassification',
        'TweetSentimentExtractionClassification',
    ],
    'Clustering': [
        # 11
        'ArxivClusteringP2P',
        'ArxivClusteringS2S',
        'BiorxivClusteringP2P',
        'BiorxivClusteringS2S',
        'MedrxivClusteringP2P',
        'MedrxivClusteringS2S',
        'RedditClustering',
        'RedditClusteringP2P',
        'StackExchangeClustering',
        'StackExchangeClusteringP2P',
        'TwentyNewsgroupsClustering',
    ],
    'PairClassification': [
        # 3
        'SprintDuplicateQuestions',
        'TwitterSemEval2015',
        'TwitterURLCorpus',
    ],
    'Reranking': [
        # 4
        'AskUbuntuDupQuestions',
        'MindSmallReranking',
        'SciDocsRR',
        'StackOverflowDupQuestions',
    ],
    'STS': [
        # 10
        'BIOSSES',
        'SICK-R',
        'STS12',
        'STS13',
        'STS14',
        'STS15',
        'STS16',
        'STS17',
        'STS22',
        'STSBenchmark',
    ],
    'Summarization': [
        # 1
        'SummEval',
    ]
}