from dataclasses import dataclass
from enum import Enum


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


# ARFBench Leaderboard columns
@dataclass(frozen=True)
class AutoEvalColumn:
    # Model column (always displayed)
    model = ColumnContent("model", "markdown", True, never_hidden=True)
    # Model type column
    model_type = ColumnContent("model_type", "str", True)
    # Performance metrics
    overall_f1 = ColumnContent("overall_f1", "number", True)
    tier_i_f1 = ColumnContent("tier_i_f1", "number", True)
    tier_ii_f1 = ColumnContent("tier_ii_f1", "number", True)
    tier_iii_f1 = ColumnContent("tier_iii_f1", "number", True)
    # Specific benchmark metrics
    presence = ColumnContent("presence", "number", True)
    identification = ColumnContent("identification", "number", True)
    start_time = ColumnContent("start_time", "number", True)
    end_time = ColumnContent("end_time", "number", True)
    magnitude = ColumnContent("magnitude", "number", True)
    categorization = ColumnContent("categorization", "number", True)
    correlation = ColumnContent("correlation", "number", True)
    indicator = ColumnContent("indicator", "number", True)


# Overall + per-tier leaderboard columns
@dataclass(frozen=True)
class OverallTierColumn:
    model = ColumnContent("model", "markdown", True, never_hidden=True)
    model_type = ColumnContent("model_type", "str", True)
    accuracy = ColumnContent("accuracy", "number", True)
    tier_i_accuracy = ColumnContent("tier_i_accuracy", "number", True)
    tier_ii_accuracy = ColumnContent("tier_ii_accuracy", "number", True)
    tier_iii_accuracy = ColumnContent("tier_iii_accuracy", "number", True)
    overall_f1 = ColumnContent("overall_f1", "number", True)
    tier_i_f1 = ColumnContent("tier_i_f1", "number", True)
    tier_ii_f1 = ColumnContent("tier_ii_f1", "number", True)
    tier_iii_f1 = ColumnContent("tier_iii_f1", "number", True)


# Per-category F1 leaderboard columns
@dataclass(frozen=True)
class CategoryF1Column:
    model = ColumnContent("model", "markdown", True, never_hidden=True)
    model_type = ColumnContent("model_type", "str", True)
    overall_f1 = ColumnContent("overall_f1", "number", True)
    presence = ColumnContent("presence", "number", True)
    identification = ColumnContent("identification", "number", True)
    start_time = ColumnContent("start_time", "number", True)
    end_time = ColumnContent("end_time", "number", True)
    magnitude = ColumnContent("magnitude", "number", True)
    categorization = ColumnContent("categorization", "number", True)
    correlation = ColumnContent("correlation", "number", True)
    indicator = ColumnContent("indicator", "number", True)


# Per-category accuracy leaderboard columns
@dataclass(frozen=True)
class CategoryAccuracyColumn:
    model = ColumnContent("model", "markdown", True, never_hidden=True)
    model_type = ColumnContent("model_type", "str", True)
    overall_accuracy = ColumnContent("overall_accuracy", "number", True)
    presence = ColumnContent("presence", "number", True)
    identification = ColumnContent("identification", "number", True)
    start_time = ColumnContent("start_time", "number", True)
    end_time = ColumnContent("end_time", "number", True)
    magnitude = ColumnContent("magnitude", "number", True)
    categorization = ColumnContent("categorization", "number", True)
    correlation = ColumnContent("correlation", "number", True)
    indicator = ColumnContent("indicator", "number", 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):
    LLM = ModelDetails(name="LLM", symbol="🟢")
    VLM = ModelDetails(name="VLM", symbol="🔶")
    TSFM = ModelDetails(name="Post-trained TSFM", 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 "VLM" in type or "🔶" in type:
            return ModelType.VLM
        if "LLM" in type or "🟢" in type:
            return ModelType.LLM
        if "TSFM" in type or "⭕" in type:
            return ModelType.TSFM
        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]

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

# Define the benchmark columns for ARFBench
BENCHMARK_COLS = [
    "model",
    "model_type",
    "overall_f1",
    "tier_i_f1",
    "tier_ii_f1",
    "tier_iii_f1",
    "presence",
    "identification",
    "start_time",
    "end_time",
    "magnitude",
    "categorization",
    "correlation",
    "indicator",
]


# New leaderboard datasets
OVERALL_TIER_COLS = [c.name for c in fields(OverallTierColumn) if not c.hidden]
CATEGORY_F1_COLS = [c.name for c in fields(CategoryF1Column) if not c.hidden]
CATEGORY_ACCURACY_COLS = [c.name for c in fields(CategoryAccuracyColumn) if not c.hidden]