from dataclasses import dataclass, make_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

## Leaderboard columns
archive_info_dict = []

archive_info_dict.append(["dataset", ColumnContent, ColumnContent("dataset", "markdown", True, never_hidden=True)])
archive_info_dict.append(["unique_id", ColumnContent, ColumnContent("unique_id", "str", True, never_hidden=True)])
archive_info_dict.append(["freq", ColumnContent, ColumnContent("freq", "str", True, never_hidden=True)])
archive_info_dict.append(["domain", ColumnContent, ColumnContent("domain", "str", True, never_hidden=True)])
# Raw features
archive_info_dict.append(["x_acf1", ColumnContent, ColumnContent("x_acf1", "number", False, False)])
archive_info_dict.append(["x_acf10", ColumnContent, ColumnContent("x_acf10", "number", False, False)])
archive_info_dict.append(["lumpiness", ColumnContent, ColumnContent("lumpiness", "number", False, False)])
archive_info_dict.append(["stability", ColumnContent, ColumnContent("stability", "number", False, False)])
archive_info_dict.append(["hurst", ColumnContent, ColumnContent("hurst", "number", False, False)])
archive_info_dict.append(["entropy", ColumnContent, ColumnContent("entropy", "number", False, False)])
# Trend features
archive_info_dict.append(["trend", ColumnContent, ColumnContent("trend_strength", "number", False, False)])
archive_info_dict.append(["trend_crossing_point_ratio", ColumnContent, ColumnContent("trend_xpoint_ratio", "number", False, False)])
archive_info_dict.append(["trend_stability", ColumnContent, ColumnContent("trend_stability", "number", False, False)])
archive_info_dict.append(["trend_lumpiness", ColumnContent, ColumnContent("trend_lumpiness", "number", False, False)])
archive_info_dict.append(["trend_hurst", ColumnContent, ColumnContent("trend_hurst", "number", False, False)])
archive_info_dict.append(["trend_entropy", ColumnContent, ColumnContent("trend_entropy", "number", False, False)])
# Seasonal features
archive_info_dict.append(["e_acf1", ColumnContent, ColumnContent("e_acf1", "number", False, False)])
archive_info_dict.append(["e_acf10", ColumnContent, ColumnContent("e_acf10", "number", False, False)])
archive_info_dict.append(["e_entropy", ColumnContent, ColumnContent("e_entropy", "number", False, False)])
archive_info_dict.append(["e_hurst", ColumnContent, ColumnContent("e_hurst", "number", False, False)])
archive_info_dict.append(["e_lumpiness", ColumnContent, ColumnContent("e_lumpiness", "number", False, False)])
archive_info_dict.append(["e_outlier_ratio", ColumnContent, ColumnContent("e_outlier_ratio", "number", False, False)])
# Remainder features
archive_info_dict.append(["seasonal_strength", ColumnContent, ColumnContent("seasonal_strength", "number", False, False)])
archive_info_dict.append(["seasonality_corr", ColumnContent, ColumnContent("seasonality_corr", "number", False, False)])
archive_info_dict.append(["seasonal_stability", ColumnContent, ColumnContent("seasonal_stability", "number", False, False)])
archive_info_dict.append(["seasonal_lumpiness", ColumnContent, ColumnContent("seasonal_lumpiness", "number", False, False)])
archive_info_dict.append(["seasonal_hurst", ColumnContent, ColumnContent("seasonal_hurst", "number", False, False)])
archive_info_dict.append(["seasonal_entropy", ColumnContent, ColumnContent("seasonal_entropy", "number", False, False)])
# Statistics
archive_info_dict.append(["mean", ColumnContent, ColumnContent("mean", "number", False, False)])
archive_info_dict.append(["std", ColumnContent, ColumnContent("std", "number", False, False)])
archive_info_dict.append(["missing_rate", ColumnContent, ColumnContent("missing_rate", "number", False, False)])
archive_info_dict.append(["length", ColumnContent, ColumnContent("length", "number", False, False)])
archive_info_dict.append(["period1", ColumnContent, ColumnContent("period1", "number", False, False)])
archive_info_dict.append(["period2", ColumnContent, ColumnContent("period2", "number", False, False)])
archive_info_dict.append(["period3", ColumnContent, ColumnContent("period3", "number", False, False)])
archive_info_dict.append(["p_strength1", ColumnContent, ColumnContent("p_strength1", "number", False, False)])
archive_info_dict.append(["p_strength2", ColumnContent, ColumnContent("p_strength2", "number", False, False)])
archive_info_dict.append(["p_strength3", ColumnContent, ColumnContent("p_strength3", "number", False, False)])

ArchiveInfoColumn = make_dataclass("ArchiveInfoColumn", archive_info_dict, frozen=True)









model_info_dict = []
# Init column for the model properties
model_info_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
model_info_dict.append(["model", ColumnContent, ColumnContent("model", "markdown", True, never_hidden=True)])
# Model information
model_info_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False, True)])
model_info_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False, True)])
model_info_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False, True)])
model_info_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False, True)])
model_info_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False, True)])
model_info_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
model_info_dict.append(["org", ColumnContent, ColumnContent("Organization", "str", True, hidden=False)])
model_info_dict.append(["testdata_leakage", ColumnContent, ColumnContent("TestData Leakage", "str", True, hidden=False)])

# We use make dataclass to dynamically fill the scores from Tasks
ModelInfoColumn = make_dataclass("ModelInfoColumn", model_info_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)
    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="🟢")
    ZT = ModelDetails(name="🔴 zero-shot", symbol="🔴")
    FT = ModelDetails(name="🟣 fine-tuned", symbol="🟣")
    AG = ModelDetails(name="🟡 agentic", symbol="🟡")
    DL = ModelDetails(name="🔷 deep-learning", symbol="🔷")
    ST = ModelDetails(name="🔶 statistical", 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 "zero-shot" in type or "🔴" in type:
            return ModelType.ZT
        if "agentic" in type or "🟡" in type:
            return ModelType.AG
        if "deep-learning" in type or "🟦" in type:
            return ModelType.DL
        if "statistical" in type or "🟣" in type:
            return ModelType.ST
        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
MODEL_INFO_COLS = [c.name for c in fields(ModelInfoColumn) 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]