from lm_eval import evaluator
from lm_eval.tasks import TaskManager

from src.backend.manage_requests import EvalRequest

from src.backend.tasks.xsum.task import XSum
from src.backend.tasks.xsum.task_v2 import XSumv2

from src.backend.tasks.cnndm.task import CNNDM
from src.backend.tasks.cnndm.task_v2 import CNNDMv2

# from src.backend.tasks.selfcheckgpt.task import SelfCheckGPT

from src.backend.huggingface_generate_until import HFLMwithChatTemplate

def run_evaluation(eval_request: EvalRequest, task_names, num_fewshot, batch_size, device, use_cache=None, limit=None, max_nb_samples=100) -> dict:
    if limit:
        print("WARNING: --limit SHOULD ONLY BE USED FOR TESTING. REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.")

    # include_task_folder("src/backend/tasks/")
    # initialize_tasks('INFO')

    print(f"Allocating task manager for: {task_names}")

    task_manager = TaskManager(include_path="./src/backend/tasks/")
    # task_manager.initialize_tasks('INFO')

    print(f"Considered Tasks: {task_names}")
    # print(f"Allowed Tasks: {tasks.ALL_TASKS}")

    # task_names = utils.pattern_match(task_names, tasks.ALL_TASKS)

    print(f"Selected Tasks: {task_names}")
    print(f"Eval Request: {eval_request.get_model_args()}")
    # hf-chat is implemented to use apply_chat_template
    results = evaluator.simple_evaluate(model="hf-auto",  # "hf-causal-experimental",  # "hf-causal", hf-chat
                                        model_args=eval_request.get_model_args(),
                                        tasks=task_names,
                                        num_fewshot=num_fewshot,
                                        batch_size=batch_size,
                                        max_batch_size=8,
                                        device=device,
                                        use_cache=use_cache,
                                        limit=limit,
                                        write_out=True,
                                        task_manager=task_manager)

    results["config"]["model_dtype"] = eval_request.precision
    results["config"]["model_name"] = eval_request.model
    results["config"]["model_sha"] = eval_request.revision

    if max_nb_samples is not None:
        if 'samples' in results:
            samples = results['samples']
            for task_name in samples.keys():
                if len(samples[task_name]) > max_nb_samples:
                    results['samples'][task_name] = results['samples'][task_name][:max_nb_samples]

    # print(evaluator.make_table(results))

    return results