from dataclasses import dataclass, make_dataclass
from dataclasses import dataclass, field   # 确保 field 被导入
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
    emoji: str = ''      # 新增：支持图标 emoji
    color: str = ''      # 新增：支持颜色

## Leaderboard columns
attrs = {}

# 静态列：model_type_symbol 使用新增的 emoji 和 color 参数
attrs['model_type_symbol'] = ColumnContent(
    name="",              # 可留空或设置为适当标题，例如 "Type Symbol"
    type="str",
    displayed_by_default=True,
    emoji='🤖',
    color='blue'
)

attrs['model'] = ColumnContent("Model", "markdown", True, never_hidden=True)

# Scores
attrs['average'] = ColumnContent("Average ⬆️", "number", True)

for task in Tasks:
    attrs[task.name] = ColumnContent(task.value.col_name, "number", True)

# Model information
attrs['model_type'] = ColumnContent("Type", "str", False)
attrs['architecture'] = ColumnContent("Architecture", "str", False)
attrs['weight_type'] = ColumnContent("Weight type", "str", False, True)
attrs['precision'] = ColumnContent("Precision", "str", False)
attrs['license'] = ColumnContent("Hub License", "str", False)
attrs['params'] = ColumnContent("#Params (B)", "number", False)
attrs['likes'] = ColumnContent("Hub ❤️", "number", False)
attrs['still_on_hub'] = ColumnContent("Available on the hub", "bool", False)
attrs['revision'] = ColumnContent("Model sha", "str", False, False)

# 动态创建 AutoEvalColumn 类，使每个列名成为类属性
AutoEvalColumn = type('AutoEvalColumn', (), attrs)

## 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]