src/display/utils.py

from dataclasses import dataclass, make_dataclass
from enum import Enum

import pandas as pd

from src.about import Tasks
from src.about import ClinicalTypes


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
    dataset_task_col: bool = False
    clinical_type_col: bool = False    


## Leaderboard columns
auto_eval_column_dict = []
# Init
auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
# 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, False, dataset_task_col=True)])
for task in ClinicalTypes:
    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True, False, clinical_type_col=True)])
# Model information
auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
auto_eval_column_dict.append(["backbone", ColumnContent, ColumnContent("Base Model", "str", False)])
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)])
auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
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("Model sha", "str", False, True)])

# 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)
    architecture = ColumnContent("model_architecture", "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):
    ZEROSHOT = ModelDetails(name="zero-shot", symbol="⚫")
    FINETUNED = ModelDetails(name="fine-tuned", symbol="⚪")
    # 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):
        if "zero-shot" in type or "⚫" in type:
            return ModelType.ZEROSHOT
        if "fine-tuned" in type or "⚪" in type:
            return ModelType.FINETUNED
        # if "fine-tuned" in type or "🔶" in type:
        #     return ModelType.FT
        # if "pretrained" in type or "🟢" in type:
        #     return ModelType.PT
        # if "RL-tuned" in type or "🟦" in type:
        #     return ModelType.RL
        # if "instruction-tuned" in type or "⭕" in type:
        #     return ModelType.IFT
        return ModelType.Unknown

class ModelArch(Enum):
    Encoder = ModelDetails("Encoder")
    Decoder = ModelDetails("Decoder")
    GLiNEREncoder = ModelDetails("GLiNER Encoder")
    Unknown = ModelDetails(name="Other", symbol="?")

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

    @staticmethod
    def from_str(type):
        if "Encoder" == type:
            return ModelArch.Encoder
        if "Decoder" == type:
            return ModelArch.Decoder
        if "GLiNER Encoder" == type:
            return ModelArch.GLiNEREncoder
        # if "unknown" in type:
        #     return ModelArch.Unknown
        return ModelArch.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


class PromptTemplateName(Enum):
    UniversalNERTemplate = "universal_ner"
    LLMHTMLHighlightedSpansTemplate = "llm_html_highlighted_spans"
    LLMHTMLHighlightedSpansTemplateV1 = "llm_html_highlighted_spans_v1"
    LLamaNERTemplate = "llama_70B_ner"
    # MixtralNERTemplate = "mixtral_ner_v0.3"

class EvaluationMetrics(Enum):
    SpanBased = "Span Based"
    TokenBased = "Token Based"


# Column selection
DATASET_COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.clinical_type_col]
Clinical_TYPES_COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.dataset_task_col]
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)]

DATASET_BENCHMARK_COLS = [t.value.col_name for t in Tasks]
TYPES_BENCHMARK_COLS = [t.value.col_name for t in ClinicalTypes]

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"),
}