src/display/utils.py

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

import pandas as pd

from src.about import Tasks, Domains

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", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
# auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])

# # new columns
# for domain in Domains:
#     auto_eval_column_dict.append([domain.name, ColumnContent, ColumnContent(domain.value.col_name, "number", True)])

# auto_eval_column_dict.append(["organization", ColumnContent, ColumnContent("Organization", "str", False)])
# auto_eval_column_dict.append(["knowledge_cutoff", ColumnContent, ColumnContent("Knowledge cutoff", "str", False)])


# for task in Tasks:
#     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
# auto_eval_column_dict.append(["model_type_symbol", ColumnContent, field(default_factory=lambda: ColumnContent("T", "str", True, never_hidden=True))])
# #Scores
# auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", 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(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
# auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "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)])
# auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
# auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])

# Init
auto_eval_column_dict.append(["model", ColumnContent, field(default_factory=lambda: ColumnContent("Model", "markdown", True, never_hidden=True))])
auto_eval_column_dict.append(["license", ColumnContent, field(default_factory=lambda: ColumnContent("License", "str", False))])

# new columns
for domain in Domains:
    auto_eval_column_dict.append([domain.name, ColumnContent, field(default_factory=lambda: ColumnContent(domain.value.col_name, "number", True))])

auto_eval_column_dict.append(["organization", ColumnContent, field(default_factory=lambda: ColumnContent("Organization", "str", False))])
auto_eval_column_dict.append(["knowledge_cutoff", ColumnContent, field(default_factory=lambda: ColumnContent("Knowledge cutoff", "str", False))])
auto_eval_column_dict.append(["score", ColumnContent, field(default_factory=lambda: ColumnContent("Average Score", "number", True))])
auto_eval_column_dict.append(["score_sd", ColumnContent, field(default_factory=lambda: ColumnContent("Score SD", "number", True))])
auto_eval_column_dict.append(["rank", ColumnContent, field(default_factory=lambda: ColumnContent("Rank", "number", True))])

# fine-graine dimensions
auto_eval_column_dict.append(["score_overall", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Overall)", "number", True))])
auto_eval_column_dict.append(["score_math_algebra", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Algebra)", "number", True))])
auto_eval_column_dict.append(["score_math_geometry", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Geometry)", "number", True))])
auto_eval_column_dict.append(["score_math_probability", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Probability)", "number", True))])
auto_eval_column_dict.append(["score_reason_logical", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Logical Reasoning)", "number", True))])
auto_eval_column_dict.append(["score_reason_social", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Social Reasoning)", "number", True))])

auto_eval_column_dict.append(["sd_overall", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev(Overall)", "number", True))])
auto_eval_column_dict.append(["sd_math_algebra", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Math Algebra)", "number", True))])
auto_eval_column_dict.append(["sd_math_geometry", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Math Geometry)", "number", True))])
auto_eval_column_dict.append(["sd_math_probability", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Math Probability)", "number", True))])
auto_eval_column_dict.append(["sd_reason_logical", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Logical Reasoning)", "number", True))])
auto_eval_column_dict.append(["sd_reason_social", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Social Reasoning)", "number", True))])

auto_eval_column_dict.append(["rank_overall", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Overall)", "number", True))])
auto_eval_column_dict.append(["rank_math_algebra", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Math Algebra)", "number", True))])
auto_eval_column_dict.append(["rank_math_geometry", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Math Geometry)", "number", True))])
auto_eval_column_dict.append(["rank_math_probability", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Math Probability)", "number", True))])
auto_eval_column_dict.append(["rank_reason_logical", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Logical Reasoning)", "number", True))])
auto_eval_column_dict.append(["rank_reason_social", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Social Reasoning)", "number", True))])
                             

for task in Tasks:
    auto_eval_column_dict.append([task.name, ColumnContent, field(default_factory=lambda: ColumnContent(task.value.col_name, "number", True))])
auto_eval_column_dict.append(["model_type_symbol", ColumnContent, field(default_factory=lambda: ColumnContent("T", "str", True, never_hidden=True))])
#Scores
auto_eval_column_dict.append(["average", ColumnContent, field(default_factory=lambda: ColumnContent("Average ⬆️", "number", True))])
# Model information
auto_eval_column_dict.append(["model_type", ColumnContent, field(default_factory=lambda: ColumnContent("Type", "str", False))])
auto_eval_column_dict.append(["architecture", ColumnContent, field(default_factory=lambda: ColumnContent("Architecture", "str", False))])
auto_eval_column_dict.append(["weight_type", ColumnContent, field(default_factory=lambda: ColumnContent("Weight type", "str", False, True))])
auto_eval_column_dict.append(["precision", ColumnContent, field(default_factory=lambda: ColumnContent("Precision", "str", False))])
auto_eval_column_dict.append(["params", ColumnContent, field(default_factory=lambda: ColumnContent("#Params (B)", "number", False))])
auto_eval_column_dict.append(["likes", ColumnContent, field(default_factory=lambda: ColumnContent("Hub ❤️", "number", False))])
auto_eval_column_dict.append(["still_on_hub", ColumnContent, field(default_factory=lambda: ColumnContent("Available on the hub", "bool", False))])
auto_eval_column_dict.append(["revision", ColumnContent, field(default_factory=lambda: ColumnContent("Model sha", "str", False, False))])

# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
AutoEvalColumn = AutoEvalColumn()
# print all attributes of AutoEvalColumn
# print(AutoEvalColumn.__annotations__.keys())
# preint precision attribute
# print(AutoEvalColumn.precision)


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

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

    def from_str(precision):
        if precision in ["torch.float16", "float16"]:
            return Precision.float16
        if precision in ["torch.bfloat16", "bfloat16"]:
            return Precision.bfloat16
        return Precision.Unknown

# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
# print(COLS)

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]