src/display/utils.py

from dataclasses import dataclass, make_dataclass
from enum import Enum

try:
    from src.about import Tasks
except ImportError:
    from about import Tasks


@dataclass(frozen=True)
class ColumnContent:
    name: str
    type: str
    displayed_by_default: bool
    hidden: bool = False
    never_hidden: bool = False


def make_auto_eval_column_dict():
    cols = []
    # "T" column set to "markdown" so it can be a clickable link
    cols.append(["model_type_symbol", ColumnContent, ColumnContent("T", "markdown", True, never_hidden=True)])
    cols.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])

    for task in Tasks:
        cols.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])

    cols.append(["sample_adequate", ColumnContent, ColumnContent("Calib. Sample OK", "str", True)])
    cols.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
    cols.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
    cols.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", True)])
    cols.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
    cols.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False)])
    return cols


auto_eval_column_dict = make_auto_eval_column_dict()
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)


def fields(raw_class=None):
    return [col[2] for col in auto_eval_column_dict]


@dataclass
class ModelDetails:
    name: str
    display_name: str = ""
    symbol: str = ""


class ModelType(Enum):
    PT = ModelDetails(name="pretrained", symbol="🟢")
    FT = ModelDetails(name="fine-tuned", symbol="🔶")
    IFT = ModelDetails(name="instruction-tuned", 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_str):
        if not type_str:
            return ModelType.Unknown
        if "fine-tuned" in type_str or "🔶" in type_str:
            return ModelType.FT
        if "pretrained" in type_str or "🟢" in type_str:
            return ModelType.PT
        if "RL-tuned" in type_str or "🟦" in type_str:
            return ModelType.RL
        if "instruction-tuned" in type_str or "⭕" in type_str:
            return ModelType.IFT
        return ModelType.Unknown


class Precision(Enum):
    bfloat16 = ModelDetails("bfloat16")
    float16 = ModelDetails("float16")
    float32 = ModelDetails("float32")
    Unknown = ModelDetails("?")

    @staticmethod
    def from_str(prec):
        if not prec:
            return Precision.Unknown
        prec_str = str(prec).lower()
        if "bfloat16" in prec_str:
            return Precision.bfloat16
        if "float16" in prec_str:
            return Precision.float16
        if "float32" in prec_str:
            return Precision.float32
        return Precision.Unknown


COLS = [c.name for c in fields()]
EVAL_COLS = [c.name for c in fields()]
EVAL_TYPES = [c.type for c in fields()]
BENCHMARK_COLS = [t.value.col_name for t in Tasks]