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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# flake8: noqa E501
from dataclasses import dataclass, field, make_dataclass
from enum import Enum
from functools import partial
import pandas as pd
from src.about import Tasks
def fields(raw_class):
return [v for k, v in raw_class.__dict__.items() if not k.startswith("__") and not k.endswith("__")]
# 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 = field(default='str')
displayed_by_default: bool = field(default=False)
hidden: bool = field(default=False)
never_hidden: bool = field(default=False)
# Helper function to create a ColumnContent with default_factory
def create_column_content(name, type='str', displayed_by_default=False, hidden=False, never_hidden=False):
return field(default_factory=lambda: ColumnContent(name, type, displayed_by_default, hidden, never_hidden))
auto_eval_column_dict = [
("model_type_symbol", ColumnContent, create_column_content(
"", "str", True, never_hidden=True)),
("model", ColumnContent, create_column_content(
"Model", "markdown", True, never_hidden=True)),
("soliditybench", ColumnContent, create_column_content("Score", "number", True)),
# ("average", ColumnContent, create_column_content("Average", "number", True)),
]
# Add task-specific columns
remaining_tasks_to_display = 3
for task in Tasks:
displayed_by_default = True
if remaining_tasks_to_display > 0:
remaining_tasks_to_display -= 1
else:
displayed_by_default = False
auto_eval_column_dict.append((
task.name,
ColumnContent,
create_column_content(
task.value.col_name,
"number",
displayed_by_default,
),
))
# Add model information columns
hide = True
display = True
model_info_columns = [
("model_type", "Type", "str", not display, not hide),
("architecture", "Architecture", "str", not display, not hide),
("weight_type", "Weight type", "str", not display, hide),
("precision", "Precision", "str", not display, not hide),
("license", "License", "str", not display, not hide),
("params", "Parameters (billions)", "number", not display, not hide),
("likes", "Likes", "number", not display, hide),
("still_on_hub", "HuggingFace Hub", "bool", not display, hide),
("revision", "Revision", "str", not display, not hide),
]
for col_name, display_name, col_type, displayed_by_default, *args in model_info_columns:
hidden = args[0] if args else False
auto_eval_column_dict.append((col_name, ColumnContent, create_column_content(
display_name, col_type, displayed_by_default, hidden)))
# Create the AutoEvalColumn dataclass
AutoEvalColumn = make_dataclass(
"AutoEvalColumn", auto_eval_column_dict, frozen=True)()
# For the requests 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)
# precision = ColumnContent("precision", "str", True)
# weight_type = ColumnContent("weight_type", "str", "Original")
# 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):
PT = ModelDetails(name="pretrained", symbol="πŸ’Ž")
FT = ModelDetails(name="finetuned", symbol="πŸ’")
BrainDAO = ModelDetails(name="braindao", 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 "finetuned" in type or "πŸ’" in type:
return ModelType.FT
if "pretrained" in type or "πŸ’Ž" in type:
return ModelType.PT
if "braindao" in type or "🧠" in type:
return ModelType.BrainDAO
return ModelType.Unknown
class WeightType(Enum):
Adapter = ModelDetails("Adapter")
Original = ModelDetails("Original")
Delta = ModelDetails("Delta")
class Precision(Enum):
float16 = ModelDetails("float16")
bfloat16 = ModelDetails("bfloat16")
qt_8bit = ModelDetails("8bit")
qt_4bit = ModelDetails("4bit")
qt_GPTQ = ModelDetails("GPTQ")
Unknown = ModelDetails("Unknown")
@staticmethod
def from_str(precision):
if precision in ["torch.float16", "float16"]:
return Precision.float16
if precision in ["torch.bfloat16", "bfloat16"]:
return Precision.bfloat16
if precision in ["8bit"]:
return Precision.qt_8bit
if precision in ["4bit"]:
return Precision.qt_4bit
if precision in ["GPTQ", "None"]:
return Precision.qt_GPTQ
return Precision.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]