from dataclasses import dataclass
from enum import Enum

import pandas as pd

from src.about import Tasks


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


BENCHMARK_DISPLAY_NAME_OVERRIDES = {
    "Scientific Figure": "Sci. Fig",
}


def benchmark_display_name(name: str) -> str:
    return BENCHMARK_DISPLAY_NAME_OVERRIDES.get(name, name)


def benchmark_internal_name(name: str) -> str:
    for internal_name, display_name in BENCHMARK_DISPLAY_NAME_OVERRIDES.items():
        if name == display_name:
            return internal_name
    return name


## Leaderboard columns
auto_eval_column_dict = []
# Core columns
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "str", True, never_hidden=True)])
# Scores
auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average", "str", True)])
auto_eval_column_dict.append(["dom_webpage", ColumnContent, ColumnContent("Webpage", "str", True)])
auto_eval_column_dict.append(["dom_poster", ColumnContent, ColumnContent("Poster", "str", True)])
auto_eval_column_dict.append(["dom_chart", ColumnContent, ColumnContent("Chart", "str", True)])
auto_eval_column_dict.append(
    ["dom_scientific_figure", ColumnContent, ColumnContent("Sci. Fig", "str", True)]
)
auto_eval_column_dict.append(["dim_layout", ColumnContent, ColumnContent("Layout", "str", True)])
auto_eval_column_dict.append(["dim_attribute", ColumnContent, ColumnContent("Attribute", "str", True)])
auto_eval_column_dict.append(["dim_text", ColumnContent, ColumnContent("Text", "str", True)])
auto_eval_column_dict.append(["dim_knowledge", ColumnContent, ColumnContent("Knowledge", "str", True)])
auto_eval_column_dict.append(["dom_slides", ColumnContent, ColumnContent("Slides", "str", True)])

# Build a dynamic class instead of dataclass defaults to keep compatibility
# with newer Python versions that reject mutable dataclass defaults.
AutoEvalColumn = type("AutoEvalColumn", (), {name: value for name, _, value in auto_eval_column_dict})


## 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]

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]