from dataclasses import dataclass, make_dataclass, field
from src.about import EvalDimensions

def fields(raw_class):
    # This helper looks at the class __dict__, so we must ensure 
    # the objects are actually sitting on the class.
    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]

@dataclass
class ColumnContent:
    name: str
    type: str
    displayed_by_default: bool
    hidden: bool = False
    never_hidden: bool = False

column_data = {
    "rank": ColumnContent("Rank", "str", True, False),
    "model_source": ColumnContent("Source", "str", True, False),
    "model_category": ColumnContent("Size", "str", True, False),
    "model": ColumnContent("Model Name", "markdown", True, never_hidden=True),
    "average_score": ColumnContent("Benchmark Score (0-10)", "number", True),
}

for eval_dim in EvalDimensions:
    is_visible = eval_dim.value.metric in ["speed", "contamination_score"]
    column_data[eval_dim.name] = ColumnContent(eval_dim.value.col_name, "number", is_visible)


auto_eval_column_dict = []
for name, content in column_data.items():
    # We use a closure to capture the specific 'content' for each field
    auto_eval_column_dict.append((
        name, 
        ColumnContent, 
        field(default_factory=lambda c=content: c)
    ))

# Create the dynamic dataclass
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)


for name, content in column_data.items():
    setattr(AutoEvalColumn, name, content)

## For the queue columns
@dataclass(frozen=True)
class EvalQueueColumn:
    model = ColumnContent("model", "markdown", True)
    revision = ColumnContent("revision", "str", True)
    status = ColumnContent("status", "str", True)

# 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 EvalDimensions]