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import hashlib
import os
import pickle
import dill
from lmms_eval.loggers.utils import _handle_non_serializable, is_serializable
from lmms_eval.utils import eval_logger
MODULE_DIR = os.path.dirname(os.path.realpath(__file__))
OVERRIDE_PATH = os.getenv("LM_HARNESS_CACHE_PATH")
PATH = OVERRIDE_PATH if OVERRIDE_PATH else f"{MODULE_DIR}/.cache"
# This should be sufficient for uniqueness
HASH_INPUT = "EleutherAI-lm-evaluation-harness"
HASH_PREFIX = hashlib.sha256(HASH_INPUT.encode("utf-8")).hexdigest()
FILE_SUFFIX = f".{HASH_PREFIX}.pickle"
def load_from_cache(file_name):
try:
path = f"{PATH}/{file_name}{FILE_SUFFIX}"
with open(path, "rb") as file:
cached_task_dict = dill.loads(file.read())
return cached_task_dict
except Exception:
eval_logger.debug(f"{file_name} is not cached, generating...")
pass
def save_to_cache(file_name, obj):
if not os.path.exists(PATH):
os.mkdir(PATH)
file_path = f"{PATH}/{file_name}{FILE_SUFFIX}"
serializable_obj = []
for item in obj:
for subitem in item:
if hasattr(subitem, "arguments"): # we need to handle the arguments specially since doc_to_visual is callable method and not serializable
serializable_arguments = tuple(arg if not callable(arg) else None for arg in subitem.arguments)
subitem.arguments = serializable_arguments
eval_logger.debug(f"Saving {file_path} to cache...")
try:
with open(file_path, "wb") as file:
file.write(dill.dumps(serializable_obj))
except (pickle.PickleError, dill.PicklingError, TypeError, AttributeError):
with open(file_path, "wb") as file:
file.write(dill.dumps([[subitem if is_serializable(subitem) else _handle_non_serializable(subitem) for subitem in item] for item in obj]))
# NOTE the "key" param is to allow for flexibility
def delete_cache(key: str = ""):
files = os.listdir(PATH)
for file in files:
if file.startswith(key) and file.endswith(FILE_SUFFIX):
file_path = f"{PATH}/{file}"
os.unlink(file_path)
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