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raise ValueError('If fewshot_as_multiturn is set, apply_chat_template must be set to True.')

if (args.num_fewshot is None or args.num_fewshot == 0) and args.fewshot_as_multiturn:

raise ValueError('If fewshot_as_multiturn is set, num_fewshot must be greater than 0.')

if args.include_path is not None:

eval_logger.info(f'Including path: {args.include_path}')

task_manager = TaskManager(args.verbosity, include_path=args.include_path)

if 'push_samples_to_hub' in evaluation_tracker_args and (not args.log_samples):

eval_logger.warning('Pushing samples to the Hub requires --log_samples to be set. Samples will not be pushed to the Hub.')

if args.limit:

eval_logger.warning(' --limit SHOULD ONLY BE USED FOR TESTING.REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.')

if args.tasks is None:

eval_logger.error('Need to specify task to evaluate.')

sys.exit()

elif args.tasks == 'list':

print(task_manager.list_all_tasks())

sys.exit()

elif args.tasks == 'list_groups':

print(task_manager.list_all_tasks(list_subtasks=False, list_tags=False))

sys.exit()

elif args.tasks == 'list_tags':

print(task_manager.list_all_tasks(list_groups=False, list_subtasks=False))

sys.exit()

elif args.tasks == 'list_subtasks':

print(task_manager.list_all_tasks(list_groups=False, list_tags=False))

sys.exit()

elif os.path.isdir(args.tasks):

import glob

task_names = []

yaml_path = os.path.join(args.tasks, '*.yaml')

for yaml_file in glob.glob(yaml_path):

config = utils.load_yaml_config(yaml_file)

task_names.append(config)

else:

task_list = args.tasks.split(',')

task_names = task_manager.match_tasks(task_list)

for task in [task for task in task_list if task not in task_names]:

if os.path.isfile(task):

config = utils.load_yaml_config(task)

task_names.append(config)

task_missing = [task for task in task_list if task not in task_names and '*' not in task]

if task_missing:

missing = ', '.join(task_missing)

eval_logger.error(f'Tasks were not found: {missing}\n{utils.SPACING}Try `lm-eval --tasks list` for list of available tasks')

raise ValueError(f"Tasks not found: {missing}. Try `lm-eval --tasks {{list_groups,list_subtasks,list_tags,list}}` to list out all available names for task groupings; only (sub)tasks; tags; or all of the above, or pass '--verbosity DEBUG' to troubleshoot task registration issues.")

if args.trust_remote_code:

eval_logger.info('Passed `--trust_remote_code`, setting environment variable `HF_DATASETS_TRUST_REMOTE_CODE=true`')

import datasets

datasets.config.HF_DATASETS_TRUST_REMOTE_CODE = True

args.model_args = args.model_args + ',trust_remote_code=True'

eval_logger.info(f'Selected Tasks: {task_names}')

request_caching_args = request_caching_arg_to_dict(cache_requests=args.cache_requests)

results = evaluator.simple_evaluate(model=args.model, model_args=args.model_args, tasks=task_names, num_fewshot=args.num_fewshot, batch_size=args.batch_size, max_batch_size=args.max_batch_size, device=args.device, use_cache=args.use_cache, limit=args.limit, check_integrity=args.check_integrity, write_out=args.write_out, log_samples=args.log_samples, evaluation_tracker=evaluation_tracker, system_instruction=args.system_instruction, apply_chat_template=args.apply_chat_template, fewshot_as_multiturn=args.fewshot_as_multiturn, gen_kwargs=args.gen_kwargs, task_manager=task_manager, verbosity=args.verbosity, predict_only=args.predict_only, random_seed=args.seed[0], numpy_random_seed=args.seed[1], torch_random_seed=args.seed[2], fewshot_random_seed=args.seed[3], **request_caching_args)

if results is not None:

if args.log_samples:

samples = results.pop('samples')

dumped = json.dumps(results, indent=2, default=handle_non_serializable, ensure_ascii=False)

if args.show_config:

print(dumped)

batch_sizes = ','.join(map(str, results['config']['batch_sizes']))

if args.wandb_args:

try:

wandb_logger.post_init(results)

wandb_logger.log_eval_result()

if args.log_samples:

wandb_logger.log_eval_samples(samples)

except Exception as e:

eval_logger.info(f'Logging to Weights and Biases failed due to {e}')

evaluation_tracker.save_results_aggregated(results=results, samples=samples if args.log_samples else None)

if args.log_samples:

for (task_name, config) in results['configs'].items():

evaluation_tracker.save_results_samples(task_name=task_name, samples=samples[task_name])

if evaluation_tracker.push_results_to_hub or evaluation_tracker.push_samples_to_hub:

evaluation_tracker.recreate_metadata_card()

print(f"{args.model} ({args.model_args}), gen_kwargs: ({args.gen_kwargs}), limit: {args.limit}, num_fewshot: {args.num_fewshot}, batch_size: {args.batch_size}{(f' ({batch_sizes})' if batch_sizes else '')}")

print(make_table(results))

if 'groups' in results:

print(make_table(results, 'groups'))

if args.wandb_args:

wandb_logger.run.finish()

if __name__ == '__main__':

cli_evaluate()

# File: lm-evaluation-harness-main/lm_eval/api/filter.py

from abc import ABC, abstractmethod

from dataclasses import dataclass

from typing import Callable, Iterable, List, Union

from lm_eval.api.instance import Instance

class Filter(ABC):

def __init__(self, **kwargs) -> None:

@abstractmethod

def apply(self, resps: Union[List, Iterable], docs: List[dict]) -> Iterable:

return resps

@dataclass

class FilterEnsemble:

name: str

filters: List[Callable[[], Filter]]