src/display/utils.py

from dataclasses import dataclass, make_dataclass, field
from enum import Enum
from typing import TypeVar

import pandas as pd

_E = TypeVar("_E", bound=Enum)


def _enum_from_str(enum_cls: type[_E], value: str, default: _E) -> _E:
    """Generic enum lookup by value name. Returns *default* on miss."""
    for member in enum_cls:
        if member.value.name == value:
            return member
    return default

def fields(raw_class):
    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]


@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str

class Tasks(Enum):
    arc = Task("arc:challenge", "acc,none", "ARC-c")
    arc_easy = Task("arc:easy", "acc,none", "ARC-e")
    boolq = Task("boolq", "acc,none", "Boolq")
    hellaswag = Task("hellaswag", "acc,none", "HellaSwag")
    lambada_openai = Task("lambada:openai", "acc,none", "Lambada")
    mmlu = Task("mmlu", "acc,none", "MMLU")
    openbookqa = Task("openbookqa", "acc,none", "Openbookqa")
    piqa = Task("piqa", "acc,none", "Piqa")
    # truthfulqa:mc1 / truthfulqa:mc2 -- ?
    truthfulqa_mc = Task("truthfulqa:mc1", "acc,none", "Truthfulqa")
    # arc:challenge ?
    # arc_challenge = Task("arc:challenge", "acc_norm,none", "Arc challenge")
    # truthfulqa = Task("truthfulqa:mc", "mc2", "TruthfulQA")
    winogrande = Task("winogrande", "acc,none", "Winogrande")
    # gsm8k = Task("gsm8k", "acc", "GSM8K")

# 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
    dummy: bool = False

auto_eval_column_list = []
# Init
auto_eval_column_list.append([
    "model_type_symbol", 
    ColumnContent, 
    field(default_factory=lambda: ColumnContent("T", "str", False, hidden=True))
])

auto_eval_column_list.append([
    "model", 
    ColumnContent, 
    field(default_factory=lambda: ColumnContent("Model", "markdown", True, never_hidden=True))
])

# Scores
auto_eval_column_list.append([
    "average", 
    ColumnContent, 
    field(default_factory=lambda: ColumnContent("Average", "number", True))
])

for task in Tasks:
    auto_eval_column_list.append([
        task.name, 
        ColumnContent, 
        field(default_factory=lambda t=task: ColumnContent(t.value.col_name, "number", True))
    ])

auto_eval_column_list.append([
    "params", 
    ColumnContent, 
    field(default_factory=lambda: ColumnContent("#Params (B)", "number", True))
])

auto_eval_column_list.append([
    "model_size", 
    ColumnContent, 
    field(default_factory=lambda: ColumnContent("#Size (G)", "number", True))
])

# Dummy column for the search bar
auto_eval_column_list.append([
    "dummy", 
    ColumnContent, 
    field(default_factory=lambda: ColumnContent("model_name_for_query", "str", False, dummy=True))
])

auto_eval_column_list.append(["model_type", ColumnContent, field(default_factory=lambda: ColumnContent("Type", "str", False, hidden=True))])
auto_eval_column_list.append(["architecture", ColumnContent, field(default_factory=lambda: ColumnContent("Architecture", "str", False))])
auto_eval_column_list.append(["weight_type", ColumnContent, field(default_factory=lambda: ColumnContent("Weight type", "str", False, True))])
auto_eval_column_list.append(["quant_type", ColumnContent, field(default_factory=lambda: ColumnContent("Quant type", "str", False))])
auto_eval_column_list.append(["precision", ColumnContent, field(default_factory=lambda: ColumnContent("Precision", "str", False))])
auto_eval_column_list.append(["weight_dtype", ColumnContent, field(default_factory=lambda: ColumnContent("Weight dtype", "str", False))])
auto_eval_column_list.append(["compute_dtype", ColumnContent, field(default_factory=lambda: ColumnContent("Compute dtype", "str", False))])
auto_eval_column_list.append(["merged", ColumnContent, field(default_factory=lambda: ColumnContent("Merged", "bool", False, hidden=True))])
auto_eval_column_list.append(["license", ColumnContent, field(default_factory=lambda: ColumnContent("Hub License", "str", False))])
auto_eval_column_list.append(["likes", ColumnContent, field(default_factory=lambda: ColumnContent("Hub ❤️", "number", False))])
auto_eval_column_list.append(["still_on_hub", ColumnContent, field(default_factory=lambda: ColumnContent("Available on the hub", "bool", False, hidden=True))])
auto_eval_column_list.append(["revision", ColumnContent, field(default_factory=lambda: ColumnContent("Model sha", "str", False, False))])
auto_eval_column_list.append(["flagged", ColumnContent, field(default_factory=lambda: ColumnContent("Flagged", "bool", False, hidden=True))])
auto_eval_column_list.append(["moe", ColumnContent, field(default_factory=lambda: ColumnContent("MoE", "bool", False, hidden=True))])
auto_eval_column_list.append(["double_quant", ColumnContent, field(default_factory=lambda: ColumnContent("Double Quant", "bool", False))])
auto_eval_column_list.append(["group_size", ColumnContent, field(default_factory=lambda: ColumnContent("Group Size", "bool", False))])
# We use make dataclass to dynamically fill the scores from Tasks
# Fixed order: [model_type_symbol, model] + [model_size, params] + sorted rest
_PINNED_AFTER_MODEL = {"model_size", "params"}
_pinned = [x for x in auto_eval_column_list[2:] if x[0] in _PINNED_AFTER_MODEL]
_rest = [x for x in auto_eval_column_list[2:] if x[0] not in _PINNED_AFTER_MODEL]
sorted_columns = sorted(_rest, key=lambda x: x[0])
sorted_auto_eval_column_list = auto_eval_column_list[:2] + _pinned + sorted_columns
AutoEvalColumn = make_dataclass("AutoEvalColumn", sorted_auto_eval_column_list, frozen=True)
auto_eval_cols = AutoEvalColumn()


@dataclass(frozen=True)
class EvalQueueColumn:  # Queue column for auto_eval
    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", False)
    status = ColumnContent("status", "str", True)
    eta = ColumnContent("eta", "str", True)
    submitted_by = ColumnContent("submitted_by", "str", True)
    submitted_time = ColumnContent("submitted_time", "str", True)

eval_queue_cols = EvalQueueColumn()


@dataclass(frozen=True)
class QuantQueueColumn:  # Queue column for auto_quant
    model = ColumnContent("model", "markdown", True)
    revision = ColumnContent("revision", "str", True)
    private = ColumnContent("private", "bool", True)
    quant_scheme = ColumnContent("quant_scheme", "str", True)
    input_dtype = ColumnContent("input_dtype", "str", True)
    status = ColumnContent("status", "str", True)
    eta = ColumnContent("eta", "str", True)
    submitted_by = ColumnContent("submitted_by", "str", True)
    submitted_time = ColumnContent("submitted_time", "str", True)


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


class ModelType(Enum):
    PT = ModelDetails(name="pretrained", symbol="🟢")
    CPT = ModelDetails(name="continuously pretrained", symbol="🟩")
    FT = ModelDetails(name="fine-tuned on domain-specific datasets", symbol="🔷")
    chat = ModelDetails(name="chat models (RLHF, DPO, IFT, ...)", symbol="🔵")
    merges = ModelDetails(name="base merges and moerges", 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 "continously pretrained" in type or "🟩" in type:
            return ModelType.CPT
        if "pretrained" in type or "🟢" in type or "quantization" in type:
            return ModelType.PT
        if any([k in type for k in ["instruction-tuned", "RL-tuned", "chat", "🟦", "⭕", "🔵"]]):
            return ModelType.chat
        if "merge" in type or "🍒" in type:
            return ModelType.merges
        return ModelType.Unknown

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

class QuantType(Enum):
    gptq = ModelDetails(name="GPTQ", symbol="🟢")
    aqlm = ModelDetails(name="AQLM", symbol="⭐")
    awq = ModelDetails(name="AWQ", symbol="🟩")
    llama_cpp = ModelDetails(name="llama.cpp", symbol="🔷")
    bnb = ModelDetails(name="bitsandbytes", symbol="🔵")
    autoround = ModelDetails(name="AutoRound", symbol="🍒")
    Unknown = ModelDetails(name="?", symbol="?")
    QuantType_None = ModelDetails(name="None", symbol="✖")


    def to_str(self, separator=" "):
        return f"{self.value.symbol}{separator}{self.value.name}"

    @staticmethod
    def from_str(quant_dtype):
        return _enum_from_str(QuantType, quant_dtype, QuantType.Unknown)



class WeightDtype(Enum):
    all = ModelDetails("All")
    int2 = ModelDetails("int2")
    int3 = ModelDetails("int3")    
    int4 = ModelDetails("int4")
    int8 = ModelDetails("int8")    
    nf4 = ModelDetails("nf4")
    fp4 = ModelDetails("fp4")
    mxfp4 = ModelDetails("mxfp4")
    nvfp4 = ModelDetails("nvfp4")
    f16 = ModelDetails("float16")
    bf16 = ModelDetails("bfloat16")
    f32 = ModelDetails("float32")

    Unknown = ModelDetails("?")

    @staticmethod
    def from_str(weight_dtype):
        return _enum_from_str(WeightDtype, weight_dtype, WeightDtype.Unknown)

class ComputeDtype(Enum):
    all = ModelDetails("All")
    fp16 = ModelDetails("float16")
    bf16 = ModelDetails("bfloat16")
    int8 = ModelDetails("int8")
    fp32 = ModelDetails("float32")


    Unknown = ModelDetails("?")

    @staticmethod
    def from_str(compute_dtype):
        return _enum_from_str(ComputeDtype, compute_dtype, ComputeDtype.Unknown)
    
    
class GroupDtype(Enum):
    group_1 = ModelDetails("-1")
    group_1024 = ModelDetails("1024")
    group_256 = ModelDetails("256")
    group_128 = ModelDetails("128")
    group_64 = ModelDetails("64")
    group_32 = ModelDetails("32")

    group_all = ModelDetails("All")

    @staticmethod
    def from_str(group_dtype):
        return _enum_from_str(GroupDtype, group_dtype, GroupDtype.group_all)    

class Precision(Enum):
    # float16 = ModelDetails("float16")
    # bfloat16 = ModelDetails("bfloat16")
    qt_2bit = ModelDetails("2bit")
    qt_3bit = ModelDetails("3bit")
    qt_4bit = ModelDetails("4bit")
    qt_8bit = ModelDetails("8bit")
    qt_16bit = ModelDetails("16bit")
    qt_32bit = ModelDetails("32bit")
    Unknown = ModelDetails("?")

    @staticmethod
    def from_str(precision):
        return _enum_from_str(Precision, precision, Precision.Unknown)




# Column selection
COLS = [c.name for c in fields(auto_eval_cols)]
TYPES = [c.type for c in fields(auto_eval_cols)]

EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]

QUANT_COLS = [c.name for c in fields(QuantQueueColumn)]
QUANT_TYPES = [c.type for c in fields(QuantQueueColumn)]

# Display-only column lists (submitted_by, private, eta, revision hidden in UI)
_QUEUE_UI_HIDDEN = {"submitted_by", "private", "eta", "revision"}
QUANT_DISPLAY_COLS = [c for c in QUANT_COLS if c not in _QUEUE_UI_HIDDEN]
QUANT_DISPLAY_TYPES = [t for c, t in zip(QUANT_COLS, QUANT_TYPES) if c not in _QUEUE_UI_HIDDEN]
EVAL_DISPLAY_COLS = [c for c in EVAL_COLS if c not in _QUEUE_UI_HIDDEN]
EVAL_DISPLAY_TYPES = [t for c, t in zip(EVAL_COLS, EVAL_TYPES) if c not in _QUEUE_UI_HIDDEN]

# Human-readable column headers shown in queue tables.  Underlying DataFrame
# columns keep the original snake_case names; only the rendered header changes.
_QUEUE_HEADER_LABELS = {
    "submitted_time": "submitted time",
}


def _queue_headers(cols: list[str]) -> list[str]:
    return [_QUEUE_HEADER_LABELS.get(c, c) for c in cols]


QUANT_DISPLAY_HEADERS = _queue_headers(QUANT_DISPLAY_COLS)
EVAL_DISPLAY_HEADERS = _queue_headers(EVAL_DISPLAY_COLS)

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"),
}

NUMERIC_MODELSIZE = {
    "?": pd.Interval(-1, 0, closed="right"),
    "~4": pd.Interval(0, 4, closed="right"),
    "~8": pd.Interval(4, 8, closed="right"),
    "~16": pd.Interval(8, 16, closed="right"),
    "~36": pd.Interval(16, 36, closed="right"),
    "~48": pd.Interval(36, 48, closed="right"),
    "~64": pd.Interval(48, 64, closed="right"),
    ">72": pd.Interval(64, 200, closed="right"),
}