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from dataclasses import dataclass, make_dataclass
from enum import Enum
import pandas as pd
from src.about import Tasks
def fields(raw_class):
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass
class ColumnContent:
name: str
type: str
displayed_by_default: bool
hidden: bool = False
never_hidden: bool = False
# Leaderboard columns
auto_eval_column_dict = []
# Init
auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent(
"T", "str", True, never_hidden=True)])
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent(
"Model", "markdown", True, never_hidden=True)])
# Scores
# auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
for task in Tasks:
auto_eval_column_dict.append(
[task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
# Model information
auto_eval_column_dict.append(
["model_type", ColumnContent, ColumnContent("Type", "str", False)])
auto_eval_column_dict.append(
["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
auto_eval_column_dict.append(
["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass(
"AutoEvalColumn", auto_eval_column_dict, frozen=True)
# For the queue columns in the submission tab
@dataclass(frozen=True)
class EvalQueueColumn: # Queue column
model = ColumnContent("model", "markdown", True)
revision = ColumnContent("revision", "str", True)
private = ColumnContent("private", "bool", True)
status = ColumnContent("status", "str", True)
# All the model information that we might need
@dataclass
class ModelDetails:
name: str
display_name: str = ""
symbol: str = "" # emoji
class ModelType(Enum):
OP = ModelDetails(name="open", symbol="🟢")
CL = ModelDetails(name="closed", symbol="⭕")
Unknown = ModelDetails(name="", symbol="?")
def to_str(self, separator=" "):
return f"{self.value.symbol}{separator}{self.value.name}"
@staticmethod
def from_str(type):
if "open" in type or "🟢" in type:
return ModelType.OP
if "closed" in type or "⭕" in type:
return ModelType.CL
return ModelType.Unknown
# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
BENCHMARK_COLS = [t.value.col_name for t in Tasks]
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