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import os
from yaml import safe_load
from huggingface_hub import HfApi
TASK_CONFIG_NAME = os.getenv("TASK_CONFIG", "pt_config")
TASK_CONFIG_PATH = os.path.join('tasks_config', TASK_CONFIG_NAME + ".yaml")
with open(TASK_CONFIG_PATH, 'r', encoding='utf-8') as f:
TASK_CONFIG = safe_load(f)
def get_config(name, default):
res = None
if name in os.environ:
res = os.environ[name]
elif 'config' in TASK_CONFIG:
res = TASK_CONFIG['config'].get(name, None)
if res is None:
return default
return res
def str2bool(v):
return str(v).lower() in ("yes", "true", "t", "1")
# clone / pull the lmeh eval data
H4_TOKEN = get_config("H4_TOKEN", None)
LEADERBOARD_NAME = get_config("LEADERBOARD_NAME", "Open LLM Leaderboard")
REPO_ID = get_config("REPO_ID", "HuggingFaceH4/open_llm_leaderboard")
QUEUE_REPO = get_config("QUEUE_REPO", "open-llm-leaderboard/requests")
DYNAMIC_INFO_REPO = get_config("DYNAMIC_INFO_REPO", "open-llm-leaderboard/dynamic_model_information")
RESULTS_REPO = get_config("RESULTS_REPO", "open-llm-leaderboard/results")
RAW_RESULTS_REPO = get_config("RAW_RESULTS_REPO", None)
PRIVATE_QUEUE_REPO = QUEUE_REPO
PRIVATE_RESULTS_REPO = RESULTS_REPO
#PRIVATE_QUEUE_REPO = "open-llm-leaderboard/private-requests"
#PRIVATE_RESULTS_REPO = "open-llm-leaderboard/private-results"
IS_PUBLIC = str2bool(get_config("IS_PUBLIC", True))
CACHE_PATH=get_config("HF_HOME", ".")
os.environ["HF_HOME"] = CACHE_PATH
if not os.access(CACHE_PATH, os.W_OK):
print(f"No write access to HF_HOME: {CACHE_PATH}. Resetting to current directory.")
CACHE_PATH = "."
os.environ["HF_HOME"] = CACHE_PATH
else:
print(f"Write access confirmed for HF_HOME")
EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
DYNAMIC_INFO_PATH = os.path.join(CACHE_PATH, "dynamic-info")
DYNAMIC_INFO_FILE_PATH = os.path.join(DYNAMIC_INFO_PATH, "model_infos.json")
EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private"
EVAL_RESULTS_PATH_PRIVATE = "eval-results-private"
PATH_TO_COLLECTION = get_config("PATH_TO_COLLECTION", "open-llm-leaderboard/llm-leaderboard-best-models-652d6c7965a4619fb5c27a03")
# Rate limit variables
RATE_LIMIT_PERIOD = int(get_config("RATE_LIMIT_PERIOD", 7))
RATE_LIMIT_QUOTA = int(get_config("RATE_LIMIT_QUOTA", 5))
HAS_HIGHER_RATE_LIMIT = get_config("HAS_HIGHER_RATE_LIMIT", "TheBloke").split(',')
TRUST_REMOTE_CODE = str2bool(get_config("TRUST_REMOTE_CODE", False))
#Set if you want to get an extra field with the average eval results from the HF leaderboard
GET_ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS = str2bool(get_config("GET_ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS", False))
ORIGINAL_HF_LEADERBOARD_RESULTS_REPO = get_config("ORIGINAL_HF_LEADERBOARD_RESULTS_REPO", "open-llm-leaderboard/results")
ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, 'original_results')
SHOW_INCOMPLETE_EVALS = str2bool(get_config("SHOW_INCOMPLETE_EVALS", False))
REQUIRE_MODEL_CARD = str2bool(get_config("REQUIRE_MODEL_CARD", True))
REQUIRE_MODEL_LICENSE = str2bool(get_config("REQUIRE_MODEL_LICENSE", True))
API = HfApi(token=H4_TOKEN)
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