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from dataclasses import dataclass, make_dataclass
from enum import Enum
import pandas as pd
from src.about import Tasks, TasksMultimodal
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 = []
auto_eval_column_dict_multimodal = []
# Init
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
auto_eval_column_dict.append(["track", ColumnContent, ColumnContent("Track", "markdown", False)])
#Scores
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(["text_average", ColumnContent, ColumnContent("Text Average", "number", True)])
auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
auto_eval_column_dict_multimodal.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
auto_eval_column_dict_multimodal.append(["track", ColumnContent, ColumnContent("Track", "markdown", False)])
for task in TasksMultimodal:
auto_eval_column_dict_multimodal.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
auto_eval_column_dict_multimodal.append(["text_average", ColumnContent, ColumnContent("Text Average", "number", True)])
auto_eval_column_dict_multimodal.append(["vision_average", ColumnContent, ColumnContent("Vision Average", "number", True)])
auto_eval_column_dict_multimodal.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
auto_eval_column_dict_multimodal.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)
AutoEvalColumnMultimodal = make_dataclass("AutoEvalColumnMultimodal", auto_eval_column_dict_multimodal, frozen=True)
## For the queue columns in the submission tab
@dataclass(frozen=True)
class EvalQueueColumn: # Queue column
model = ColumnContent("model", "markdown", True)
track = ColumnContent("track", "str", 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
# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
COLS_MULTIMODAL = [c.name for c in fields(AutoEvalColumnMultimodal) 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]
BENCHMARK_COLS_MULTIMODAL = [t.value.col_name for t in TasksMultimodal] |