import json
import os
import re
import numpy as np
from collections import defaultdict
from datetime import datetime, timedelta, timezone
import huggingface_hub
from huggingface_hub import ModelCard
from huggingface_hub.hf_api import ModelInfo
from transformers import AutoConfig
from transformers.models.auto.tokenization_auto import AutoTokenizer
from src.display.utils import TEXT_TASKS, VISION_TASKS, NUM_EXPECTED_EXAMPLES
def check_model_card(repo_id: str) -> tuple[bool, str]:
"""Checks if the model card and license exist and have been filled"""
try:
card = ModelCard.load(repo_id)
except huggingface_hub.utils.EntryNotFoundError:
return False, "Please add a model card to your model to explain how you trained/fine-tuned it."
# Enforce license metadata
if card.data.license is None:
if not ("license_name" in card.data and "license_link" in card.data):
return False, (
"License not found. Please add a license to your model card using the `license` metadata or a"
" `license_name`/`license_link` pair."
)
# Enforce card content
if len(card.text) < 200:
return False, "Please add a description to your model card, it is too short."
return True, ""
def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str]:
"""Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
try:
config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
if test_tokenizer:
try:
tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
except ValueError as e:
return (
False,
f"uses a tokenizer which is not in a transformers release: {e}",
None
)
except Exception as e:
return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None)
return True, None, config
except ValueError:
return (
False,
"needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
None
)
except Exception as e:
return False, "was not found on hub!", None
def get_model_size(model_info: ModelInfo, precision: str):
"""Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
try:
model_size = round(model_info.safetensors["total"] / 1e9, 3)
except (AttributeError, TypeError):
return 0 # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
model_size = size_factor * model_size
return model_size
def get_model_arch(model_info: ModelInfo):
"""Gets the model architecture from the configuration"""
return model_info.config.get("architectures", "Unknown")
def already_submitted_models(requested_models_dir: str) -> set[str]:
"""Gather a list of already submitted models to avoid duplicates"""
depth = 1
file_names = []
users_to_submission_dates = defaultdict(list)
for root, _, files in os.walk(requested_models_dir):
current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
if current_depth == depth:
for file in files:
if not file.endswith(".json"):
continue
with open(os.path.join(root, file), "r") as f:
info = json.load(f)
file_names.append(f"{info['model']}_{info['revision']}_{info['track']}")
# Select organisation
if info["model"].count("/") == 0 or "submitted_time" not in info:
continue
organisation, _ = info["model"].split("/")
users_to_submission_dates[organisation].append(info["submitted_time"])
return set(file_names), users_to_submission_dates
def is_valid_predictions(predictions: dict) -> tuple[bool, str]:
out_msg = ""
for task in TEXT_TASKS:
if task not in predictions:
out_msg = f"Error: {task} not present"
break
for subtask in TEXT_TASKS[task]:
if subtask not in predictions[task]:
out_msg = f"Error: {subtask} not present under {task}"
break
if out_msg != "":
break
if "vqa" in predictions or "winoground" in predictions or "devbench" in predictions:
for task in VISION_TASKS:
if task not in predictions:
out_msg = f"Error: {task} not present"
break
for subtask in VISION_TASKS[task]:
if subtask not in predictions[task]:
out_msg = f"Error: {subtask} not present under {task}"
break
if out_msg != "":
break
# Make sure all examples have predictions, and that predictions are the correct type
for task in predictions:
for subtask in predictions[task]:
if task == "devbench":
a = np.array(predictions[task][subtask]["predictions"])
if subtask == "sem-things":
required_shape = (1854, 1854)
elif subtask == "gram-trog":
required_shape = (76, 4, 1)
elif subtask == "lex-viz_vocab":
required_shape = (119, 4, 1)
if a.shape[0] != required_shape[0] or a.shape[1] != required_shape[1]:
out_msg = f"Error: Wrong shape for results for `{subtask}` in `{task}`."
break
if not str(a.dtype).startswith("float"):
out_msg = f"Error: Results for `{subtask}` ({task}) \
should be floats but aren't."
break
continue
num_expected_examples = NUM_EXPECTED_EXAMPLES[task][subtask]
if len(predictions[task][subtask]["predictions"]) != num_expected_examples:
out_msg = f"Error: {subtask} has the wrong number of examples."
break
if task == "glue":
if type(predictions[task][subtask]["predictions"][0]["pred"]) != int:
out_msg = f"Error: results for `{subtask}` (`{task}`) should be integers but aren't."
break
else:
if type(predictions[task][subtask]["predictions"][0]["pred"]) != str:
out_msg = f"Error: results for `{subtask}` (`{task}`) should be strings but aren't."
break
if out_msg != "":
break
if out_msg != "":
return False, out_msg
return True, "Upload successful."