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import re
import os
import glob
import json
import os
from typing import List
from tqdm import tqdm
from src.utils_display import AutoEvalColumn, model_hyperlink
from src.auto_leaderboard.model_metadata_type import ModelType, model_type_from_str, MODEL_TYPE_METADATA
from src.auto_leaderboard.model_metadata_flags import FLAGGED_MODELS, DO_NOT_SUBMIT_MODELS
from huggingface_hub import HfApi
import huggingface_hub
api = HfApi(token=os.environ.get("H4_TOKEN", None))
def get_model_infos_from_hub(leaderboard_data: List[dict]):
for model_data in tqdm(leaderboard_data):
model_name = model_data["model_name_for_query"]
try:
model_info = api.model_info(model_name)
except huggingface_hub.utils._errors.RepositoryNotFoundError:
print("Repo not found!", model_name)
model_data[AutoEvalColumn.license.name] = None
model_data[AutoEvalColumn.likes.name] = None
model_data[AutoEvalColumn.params.name] = get_model_size(model_name, None)
continue
model_data[AutoEvalColumn.license.name] = get_model_license(model_info)
model_data[AutoEvalColumn.likes.name] = get_model_likes(model_info)
model_data[AutoEvalColumn.params.name] = get_model_size(model_name, model_info)
def get_model_license(model_info):
try:
return model_info.cardData["license"]
except Exception:
return None
def get_model_likes(model_info):
return model_info.likes
size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
def get_model_size(model_name, model_info):
# In billions
try:
return round(model_info.safetensors["total"] / 1e9, 3)
except AttributeError:
try:
size_match = re.search(size_pattern, model_name.lower())
size = size_match.group(0)
return round(float(size[:-1]) if size[-1] == "b" else float(size[:-1]) / 1e3, 3)
except AttributeError:
return None
def get_model_type(leaderboard_data: List[dict]):
for model_data in leaderboard_data:
request_files = os.path.join("eval-queue", model_data["model_name_for_query"] + "_eval_request_*" + ".json")
request_files = glob.glob(request_files)
# Select correct request file (precision)
request_file = ""
if len(request_files) == 1:
request_file = request_files[0]
elif len(request_files) > 1:
request_files = sorted(request_files, reverse=True)
for tmp_request_file in request_files:
with open(tmp_request_file, "r") as f:
req_content = json.load(f)
if req_content["status"] == "FINISHED" and req_content["precision"] == model_data["Precision"].split(".")[-1]:
request_file = tmp_request_file
if request_file == "":
model_data[AutoEvalColumn.model_type.name] = ""
model_data[AutoEvalColumn.model_type_symbol.name] = ""
continue
try:
with open(request_file, "r") as f:
request = json.load(f)
is_delta = request["weight_type"] != "Original"
except Exception:
is_delta = False
try:
with open(request_file, "r") as f:
request = json.load(f)
model_type = model_type_from_str(request["model_type"])
model_data[AutoEvalColumn.model_type.name] = model_type.value.name
model_data[AutoEvalColumn.model_type_symbol.name] = model_type.value.symbol #+ ("🔺" if is_delta else "")
except KeyError:
if model_data["model_name_for_query"] in MODEL_TYPE_METADATA:
model_data[AutoEvalColumn.model_type.name] = MODEL_TYPE_METADATA[model_data["model_name_for_query"]].value.name
model_data[AutoEvalColumn.model_type_symbol.name] = MODEL_TYPE_METADATA[model_data["model_name_for_query"]].value.symbol #+ ("🔺" if is_delta else "")
else:
model_data[AutoEvalColumn.model_type.name] = ModelType.Unknown.value.name
model_data[AutoEvalColumn.model_type_symbol.name] = ModelType.Unknown.value.symbol
def flag_models(leaderboard_data:List[dict]):
for model_data in leaderboard_data:
if model_data["model_name_for_query"] in FLAGGED_MODELS:
issue_num = FLAGGED_MODELS[model_data["model_name_for_query"]].split("/")[-1]
issue_link = model_hyperlink(FLAGGED_MODELS[model_data["model_name_for_query"]], f"See discussion #{issue_num}")
model_data[AutoEvalColumn.model.name] = f"{model_data[AutoEvalColumn.model.name]} has been flagged! {issue_link}"
def remove_forbidden_models(leaderboard_data: List[dict]):
indices_to_remove = []
for ix, model in enumerate(leaderboard_data):
if model["model_name_for_query"] in DO_NOT_SUBMIT_MODELS:
indices_to_remove.append(ix)
for ix in reversed(indices_to_remove):
leaderboard_data.pop(ix)
return leaderboard_data
def apply_metadata(leaderboard_data: List[dict]):
leaderboard_data = remove_forbidden_models(leaderboard_data)
get_model_type(leaderboard_data)
get_model_infos_from_hub(leaderboard_data)
flag_models(leaderboard_data)
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