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

import pandas as pd


def fields(raw_class):
    return [
        v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"
    ]


@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


class Tasks(Enum):
    medmcqa = Task("medmcqa", "acc", "MedMCQA")
    medqa = Task("medqa_4options", "acc", "MedQA")

    mmlu_anatomy = Task("anatomy (mmlu)", "acc", "MMLU Anatomy")
    mmlu_ck = Task("clinical_knowledge (mmlu)", "acc", "MMLU Clinical Knowledge")
    mmlu_cb = Task("college_biology (mmlu)", "acc", "MMLU College Biology")
    mmlu_cm = Task("college_medicine (mmlu)", "acc", "MMLU College Medicine")
    mmlu_mg = Task("medical_genetics (mmlu)", "acc", "MMLU Medical Genetics")
    mmlu_pm = Task("professional_medicine (mmlu)", "acc", "MMLU Professional Medicine")
    pubmedqa = Task("pubmedqa", "acc", "PubMedQA")


# 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
    dummy: bool = False
    is_task: bool = False


# Define a function to generate ColumnContent instances
def column_content_factory(
    name: str,
    type: str,
    displayed_by_default: bool,
    hidden: bool = False,
    never_hidden: bool = False,
    dummy: bool = False,
    is_task: bool = False,
):
    return lambda: ColumnContent(
        name=name,
        type=type,
        displayed_by_default=displayed_by_default,
        hidden=hidden,
        never_hidden=never_hidden,
        dummy=dummy,
        is_task=is_task,
    )


auto_eval_column_dict = []
# Init
auto_eval_column_dict.append(
    [
        "model_type_symbol",
        ColumnContent,
        ColumnContent("T", "str", True, never_hidden=True),
    ]
)
auto_eval_column_dict.append(
    [
        "model",
        ColumnContent,
        ColumnContent("Model", "markdown", True, never_hidden=True),
    ]
)
# Scores
auto_eval_column_dict.append(
    ["average", ColumnContent, ColumnContent("Avg", "number", True)]
)
for task in Tasks:
    auto_eval_column_dict.append(
        [
            task.name,
            ColumnContent,
            ColumnContent(task.value.col_name, "number", True, is_task=True),
        ]
    )  # hidden was true by default
# 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(
    ["license", ColumnContent, ColumnContent("Hub License", "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)]
)
# Dummy column for the search bar (hidden by the custom CSS)
# Define the structure of your dataclass fields with default_factory for mutable defaults
auto_eval_column_fields = [
    (
        "model_type_symbol",
        ColumnContent,
        field(
            default_factory=column_content_factory("T", "str", True, never_hidden=True)
        ),
    ),
    # Add other fields similarly...
]

# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_fields, frozen=True)


@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)


@dataclass
class ModelDetails:
    name: str
    symbol: str = ""  # emoji, only for the model type


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):
    float32 = ModelDetails("float32")
    float16 = ModelDetails("float16")
    bfloat16 = ModelDetails("bfloat16")
    qt_8bit = ModelDetails("8bit")
    qt_4bit = ModelDetails("4bit")
    qt_GPTQ = ModelDetails("GPTQ")
    Unknown = ModelDetails("?")

    @staticmethod
    def from_str(precision: str):
        if precision in ["torch.float32", "float32"]:
            return Precision.float32
        if precision in ["torch.float16", "float16"]:
            return Precision.float16
        if precision in ["torch.bfloat16", "bfloat16"]:
            return Precision.bfloat16
        if precision in ["8bit"]:
            return Precision.qt_8bit
        if precision in ["4bit"]:
            return Precision.qt_4bit
        if precision in ["GPTQ", "None"]:
            return Precision.qt_GPTQ
        return Precision.Unknown


# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [
    c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden
]
TYPES_LITE = [
    c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and 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]

NUMERIC_INTERVALS = {
    "?": pd.Interval(-1, 0, closed="right"),
    "~1.5": pd.Interval(0, 2, closed="right"),
    "~3": pd.Interval(2, 4, closed="right"),
    "~7": pd.Interval(4, 9, closed="right"),
    "~13": pd.Interval(9, 20, closed="right"),
    "~35": pd.Interval(20, 45, closed="right"),
    "~60": pd.Interval(45, 70, closed="right"),
    "70+": pd.Interval(70, 10000, closed="right"),
}