from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets.ruler.ruler_niah import RulerNiahDataset
from opencompass.datasets.ruler.ruler_niah import RulerNiahEvaluator


# Ruler Dataset settings
niah_configurations = [
    {
        'abbr': 'single_1',
        'type_haystack': 'repeat',
        'type_needle_k': 'words',
        'type_needle_v': 'numbers',
        'num_needle_k': 1,
        'num_needle_v': 1,
        'num_needle_q': 1,
    },
    {
        'abbr': 'single_2',
        'type_haystack': 'essay',
        'type_needle_k': 'words',
        'type_needle_v': 'numbers',
        'num_needle_k': 1,
        'num_needle_v': 1,
        'num_needle_q': 1,
    },
    {
        'abbr': 'single_3',
        'type_haystack': 'essay',
        'type_needle_k': 'words',
        'type_needle_v': 'uuids',
        'num_needle_k': 1,
        'num_needle_v': 1,
        'num_needle_q': 1,
    },
    {
        'abbr': 'multikey_1',
        'type_haystack': 'essay',
        'type_needle_k': 'words',
        'type_needle_v': 'numbers',
        'num_needle_k': 4,
        'num_needle_v': 1,
        'num_needle_q': 1,
    },
    {
        'abbr': 'multikey_2',
        'type_haystack': 'needle',
        'type_needle_k': 'words',
        'type_needle_v': 'numbers',
        'num_needle_k': 1,
        'num_needle_v': 1,
        'num_needle_q': 1,
    },
    {
        'abbr': 'multikey_3',
        'type_haystack': 'needle',
        'type_needle_k': 'uuids',
        'type_needle_v': 'uuids',
        'num_needle_k': 1,
        'num_needle_v': 1,
        'num_needle_q': 1,
    },
    {
        'abbr': 'multivalue',
        'type_haystack': 'essay',
        'type_needle_k': 'words',
        'type_needle_v': 'numbers',
        'num_needle_k': 1,
        'num_needle_v': 4,
        'num_needle_q': 1,
    },
    {
        'abbr': 'multiquery',
        'type_haystack': 'essay',
        'type_needle_k': 'words',
        'type_needle_v': 'numbers',
        'num_needle_k': 1,
        'num_needle_v': 1,
        'num_needle_q': 4,
    },
]

niah_datasets = []

# NIAH Dataset
base_path = './data/ruler'
file_path = 'PaulGrahamEssays.jsonl'
for index, config in enumerate(niah_configurations):
    dataset_dict = {
        'abbr': f'ruler_niah_{config["abbr"]}',
        'type': RulerNiahDataset,
        'base_path': base_path,
        'file_path': file_path,
        # 'tokenizer_model': model_path,
        'tokens_to_generate': 128,
        # 'max_seq_length': max_seq_len,
        # 'num_samples': NUM_SAMPLES,
        'type_haystack': config['type_haystack'],
        'type_needle_k': config['type_needle_k'],
        'type_needle_v': config['type_needle_v'],
        'num_needle_k': config['num_needle_k'],
        'num_needle_v': config['num_needle_v'],
        'num_needle_q': config['num_needle_q'],
        'reader_cfg': dict(input_columns=['prompt'], output_column='answer'),
        'infer_cfg': dict(
            prompt_template=dict(
                type=PromptTemplate,
                template=dict(
                    round=[
                        dict(role='HUMAN', prompt='{prompt}'),
                        dict(role='BOT', prompt='{answer}\n'),
                    ]
                ),
            ),
            retriever=dict(type=ZeroRetriever),
            inferencer=dict(type=GenInferencer),
        ),
        'eval_cfg': dict(
            evaluator=dict(type=RulerNiahEvaluator),
        ),
    }
    niah_datasets.append(dataset_dict)