src/display/utils.py

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", False, never_hidden=True)])
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent(
    "Model", "markdown", False, never_hidden=True)])
auto_eval_column_dict.append(
    ["params", ColumnContent, ColumnContent("Model Size", "str", False, False)])
# 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, True)])
auto_eval_column_dict.append(
    ["architecture", ColumnContent, ColumnContent("Architecture", "str", False, True)])
auto_eval_column_dict.append(
    ["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
auto_eval_column_dict.append(
    ["precision", ColumnContent, ColumnContent("Precision", "str", False, True)])
auto_eval_column_dict.append(
    ["license", ColumnContent, ColumnContent("Hub License", "str", False, True)])
auto_eval_column_dict.append(
    ["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False, True)])
auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent(
    "Available on the hub", "bool", False, True)])
auto_eval_column_dict.append(
    ["revision", ColumnContent, ColumnContent("Eval Date", "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)
    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):
    open = ModelDetails(name="Open", symbol="🟢")
    # FT = ModelDetails(name="fine-tuned", symbol="🔶")
    close = ModelDetails(name="Closed", symbol="⭕")
    # RL = ModelDetails(name="RL-tuned", 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 "fine-tuned" in type or "🔶" in type:
        #     return ModelType.FT
        if "Open" in type or "🟢" in type:
            return ModelType.open
        # if "RL-tuned" in type or "🟦" in type:
        #     return ModelType.RL
        if "Closed" in type or "⭕" in type:
            return ModelType.close
        return ModelType.Unknown


class WeightType(Enum):
    Adapter = ModelDetails("Adapter")
    Original = ModelDetails("Original")
    Delta = ModelDetails("Delta")


class Precision(Enum):
    float16 = ModelDetails("float16")
    bfloat16 = ModelDetails("bfloat16")
    float32 = ModelDetails("float32")
    # qt_8bit = ModelDetails("8bit")
    # qt_4bit = ModelDetails("4bit")
    # qt_GPTQ = ModelDetails("GPTQ")
    Unknown = ModelDetails("?")

    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 ["float32"]:
            return Precision.float32
        # 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]
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [c.name for c in fields(
    AutoEvalColumn) if c.displayed_by_default and not c.hidden]
TYPES_LITE = [c.type for c in fields(
    AutoEvalColumn) if c.displayed_by_default and 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]

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"),
}

SIZE_INTERVALS = [
    'Small',
    'Medium',
    'Large',
]