main.py

import os
import json
import fnmatch
import argparse

from lm_eval import evaluator, utils
from lm_eval.tasks import ALL_TASKS
from lm_eval.logger import eval_logger

os.environ['TOKENIZERS_PARALLELISM'] = 'false'

class MultiChoice:
    def __init__(self, choices):
        self.choices = choices

    # Simple wildcard support (linux filename patterns)
    def __contains__(self, values):
        for value in values.split(","):
            if len(fnmatch.filter(self.choices, value)) == 0:
                eval_logger.warning("{} is not in task list.".format(value))
                # eval_logger.info(f"{choices} is this")

        return True

    def __iter__(self):
        for choice in self.choices:
            yield choice


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", required=True)
    parser.add_argument("--model_args", default="")
    parser.add_argument("--tasks", default=None, choices=MultiChoice(ALL_TASKS))
    parser.add_argument("--config", default=None)
    parser.add_argument("--provide_description", action="store_true")
    parser.add_argument("--num_fewshot", type=int, default=0)
    parser.add_argument("--batch_size", type=int, default=1)
    parser.add_argument("--device", type=str, default=None)
    parser.add_argument("--output_path", default=None)
    parser.add_argument("--limit", type=int, default=None)
    parser.add_argument("--no_cache", action="store_true")
    parser.add_argument("--decontamination_ngrams_path", default=None)
    parser.add_argument("--description_dict_path", default=None)
    parser.add_argument("--check_integrity", action="store_true")
    return parser.parse_args()


# Returns a list containing all values of the source_list that
# match at least one of the patterns
def pattern_match(patterns, source_list):
    task_names = set()
    for pattern in patterns:
        for matching in fnmatch.filter(source_list, pattern):
            task_names.add(matching)
    return list(task_names)


def main():
    args = parse_args()

    if args.limit:
        eval_logger.warning(
            " --limit SHOULD ONLY BE USED FOR TESTING."
            "REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT."
        )

    if args.tasks != None:
        if 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:
            tasks_list = args.tasks.split(",")
            task_names = pattern_match(tasks_list, ALL_TASKS)
            for task in [task for task in tasks_list if task not in task_names]:
                if os.path.isfile(task):
                    config = utils.load_yaml_config(task)
                    task_names.append(config)

    #     # Tas
    #     if args.config:
    #         task_names = []
    #         for config_files in args.config.split(","):
    #             config = utils.get_yaml_config(config_files)

    #             if args.num_fewshot != 0:
    #                 config["num_fewshot"] = args.num_fewshot

    #             if args.batch_size != None:
    #                 config["batch_size"] = args.batch_size

    #             task_names.append(config)
    #     else:
    #         task_names = ALL_TASKS
    # else:
        
    eval_logger.info(f"Selected Tasks: {task_names}")

    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,
        device=args.device,
        limit=args.limit,
        decontamination_ngrams_path=args.decontamination_ngrams_path,
        check_integrity=args.check_integrity,
    )

    dumped = json.dumps(results, indent=2)
    print(dumped)

    if args.output_path:
        with open(args.output_path, "w") as f:
            f.write(dumped)

    print(
        f"{args.model} ({args.model_args}), limit: {args.limit}, provide_description: {args.provide_description}, "
        f"num_fewshot: {args.num_fewshot}, batch_size: {args.batch_size}"
    )
    print(evaluator.make_table(results))


if __name__ == "__main__":
    main()