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from dataclasses import dataclass, make_dataclass
from enum import Enum
import json
import logging
from datetime import datetime
import pandas as pd
# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
# Convert ISO 8601 dates to datetime objects for comparison
def parse_iso8601_datetime(date_str):
if date_str.endswith('Z'):
date_str = date_str[:-1] + '+00:00'
return datetime.fromisoformat(date_str)
def parse_datetime(datetime_str):
formats = [
"%Y-%m-%dT%H-%M-%S.%f", # Format with dashes
"%Y-%m-%dT%H:%M:%S.%f", # Standard format with colons
"%Y-%m-%dT%H %M %S.%f", # Spaces as separator
]
for fmt in formats:
try:
return datetime.strptime(datetime_str, fmt)
except ValueError:
continue
# in rare cases set unix start time for files with incorrect time (legacy files)
logging.error(f"No valid date format found for: {datetime_str}")
return datetime(1970, 1, 1)
def load_json_data(file_path):
"""Safely load JSON data from a file."""
try:
with open(file_path, "r") as file:
return json.load(file)
except json.JSONDecodeError:
print(f"Error reading JSON from {file_path}")
return None # Or raise an exception
def fields(raw_class):
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
column_map = {
"T": "T",
"model": "Model",
"type": "Model Type",
"size_range": "Size Range",
"complete": "Complete",
"instruct": "Instruct",
"average": "Average",
"elo_mle": "Elo Rating",
"link": "Link",
"act_param": "#Act Params (B)",
"size": "#Params (B)",
"moe": "MoE",
"lazy": "Lazy",
"openness": "Openness",
"direct_complete": "Direct Completion",
}
type_map = {
"🔶": "🔶 Chat Models (RLHF, DPO, IFT, ...)",
"🟢": "🟢 Base Models"
}
moe_map = {
True: "MoE",
False: "Dense"
}
# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass(frozen=True)
class ColumnContent:
name: str
type: str
displayed_by_default: bool
hidden: bool = False
never_hidden: bool = False
dummy: bool = False
auto_eval_column_dict = []
# Init
auto_eval_column_dict.append(["T", ColumnContent, ColumnContent(column_map["T"], "str", True, never_hidden=True)])
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent(column_map["model"], "markdown", True, never_hidden=True)])
auto_eval_column_dict.append(["type", ColumnContent, ColumnContent(column_map["type"], "str", False, True)])
auto_eval_column_dict.append(["size_range", ColumnContent, ColumnContent(column_map["size_range"], "str", False, True)])
# Scores
auto_eval_column_dict.append(["complete", ColumnContent, ColumnContent(column_map["complete"], "number", True)])
auto_eval_column_dict.append(["instruct", ColumnContent, ColumnContent(column_map["instruct"], "number", True)])
auto_eval_column_dict.append(["average", ColumnContent, ColumnContent(column_map["average"], "number", True)])
auto_eval_column_dict.append(["elo_mle", ColumnContent, ColumnContent(column_map["elo_mle"], "number", True)])
# Model information
auto_eval_column_dict.append(["act_param", ColumnContent, ColumnContent(column_map["act_param"], "number", True)])
auto_eval_column_dict.append(["link", ColumnContent, ColumnContent(column_map["link"], "str", False, True)])
auto_eval_column_dict.append(["size", ColumnContent, ColumnContent(column_map["size"], "number", False)])
auto_eval_column_dict.append(["lazy", ColumnContent, ColumnContent(column_map["lazy"], "bool", False, True)])
auto_eval_column_dict.append(["moe", ColumnContent, ColumnContent(column_map["moe"], "str", False, True)])
auto_eval_column_dict.append(["openness", ColumnContent, ColumnContent(column_map["openness"], "str", False, True)])
auto_eval_column_dict.append(["direct_complete", ColumnContent, ColumnContent(column_map["direct_complete"], "bool", False)])
# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
@dataclass(frozen=True)
class EvalQueueColumn: # Queue column
model_link = ColumnContent("link", "markdown", True)
model_name = ColumnContent("model", "str", True)
@dataclass
class ModelDetails:
name: str
symbol: str = "" # emoji, only for the model type
# Column selection
COLS = [c.name for c in fields(AutoEvalColumn)]
TYPES = [c.type for c in fields(AutoEvalColumn)]
EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
NUMERIC_INTERVALS = {
"?": pd.Interval(-1, 0, closed="right"),
"~1.5": pd.Interval(0, 2, closed="right"),
"~3": pd.Interval(2, 4, closed="right"),
"~7": pd.Interval(4, 9, closed="right"),
"~13": pd.Interval(9, 20, closed="right"),
"~35": pd.Interval(20, 45, closed="right"),
"~60": pd.Interval(45, 70, closed="right"),
"70+": pd.Interval(70, 10000, closed="right"),
}
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