"""Code generated by Speakeasy (https://speakeasy.com). DO NOT EDIT."""

from __future__ import annotations
from openrouter.types import BaseModel, Nullable, UNSET_SENTINEL
from pydantic import model_serializer
from typing import Literal
from typing_extensions import TypedDict


class EloBoundsTypedDict(TypedDict):
    r"""ELO range across all returned models for normalization."""

    max: float
    r"""Maximum ELO in the result set."""
    min: float
    r"""Minimum ELO in the result set."""


class EloBounds(BaseModel):
    r"""ELO range across all returned models for normalization."""

    max: float
    r"""Maximum ELO in the result set."""

    min: float
    r"""Minimum ELO in the result set."""


BenchmarksDAMetaSource = Literal["design-arena",]
r"""Data source identifier."""


BenchmarksDAMetaSourceURL = Literal["https://www.designarena.ai",]
r"""URL of the upstream data source."""


BenchmarksDAMetaVersion = Literal["v1",]
r"""Dataset version."""


class BenchmarksDAMetaTypedDict(TypedDict):
    arena: str
    r"""The arena filter applied."""
    as_of: str
    r"""ISO-8601 timestamp of when this data was generated."""
    category: Nullable[str]
    r"""The category filter applied, or null if showing all."""
    citation: str
    r"""Required attribution when republishing this data."""
    elo_bounds: EloBoundsTypedDict
    r"""ELO range across all returned models for normalization."""
    model_count: int
    r"""Number of unique models in the response."""
    source: BenchmarksDAMetaSource
    r"""Data source identifier."""
    source_url: BenchmarksDAMetaSourceURL
    r"""URL of the upstream data source."""
    version: BenchmarksDAMetaVersion
    r"""Dataset version."""


class BenchmarksDAMeta(BaseModel):
    arena: str
    r"""The arena filter applied."""

    as_of: str
    r"""ISO-8601 timestamp of when this data was generated."""

    category: Nullable[str]
    r"""The category filter applied, or null if showing all."""

    citation: str
    r"""Required attribution when republishing this data."""

    elo_bounds: EloBounds
    r"""ELO range across all returned models for normalization."""

    model_count: int
    r"""Number of unique models in the response."""

    source: BenchmarksDAMetaSource
    r"""Data source identifier."""

    source_url: BenchmarksDAMetaSourceURL
    r"""URL of the upstream data source."""

    version: BenchmarksDAMetaVersion
    r"""Dataset version."""

    @model_serializer(mode="wrap")
    def serialize_model(self, handler):
        optional_fields = []
        nullable_fields = ["category"]
        null_default_fields = []

        serialized = handler(self)

        m = {}

        for n, f in type(self).model_fields.items():
            k = f.alias or n
            val = serialized.get(k)
            serialized.pop(k, None)

            optional_nullable = k in optional_fields and k in nullable_fields
            is_set = (
                self.__pydantic_fields_set__.intersection({n})
                or k in null_default_fields
            )  # pylint: disable=no-member

            if val is not None and val != UNSET_SENTINEL:
                m[k] = val
            elif val != UNSET_SENTINEL and (
                not k in optional_fields or (optional_nullable and is_set)
            ):
                m[k] = val

        return m