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96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
"""Code generated by Speakeasy (https://speakeasy.com). DO NOT EDIT."""
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from __future__ import annotations
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from openrouter.types import BaseModel, Nullable, UNSET_SENTINEL, UnrecognizedStr
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from openrouter.utils import validate_open_enum
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from pydantic import model_serializer
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from pydantic.functional_validators import PlainValidator
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from typing import Literal, Union
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from typing_extensions import Annotated, TypedDict
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UnifiedBenchmarksMetaSource = Union[
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Literal[
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"artificial-analysis",
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"design-arena",
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],
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UnrecognizedStr,
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]
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r"""The source filter applied, or null when all sources are returned."""
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UnifiedBenchmarksMetaVersion = Literal["v1",]
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r"""Dataset version."""
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class UnifiedBenchmarksMetaTypedDict(TypedDict):
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as_of: str
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r"""ISO-8601 timestamp of when this data was last updated."""
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citation: Nullable[str]
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r"""Required attribution when republishing this data, or null when results span multiple sources (attribute each item individually by its `source` discriminator)."""
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model_count: int
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r"""Number of unique models in the response."""
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source: Nullable[UnifiedBenchmarksMetaSource]
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r"""The source filter applied, or null when all sources are returned."""
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source_url: Nullable[str]
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r"""URL of the upstream data source, or null when results span multiple sources."""
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task_type: Nullable[str]
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r"""The task_type filter applied, or null if showing all."""
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version: UnifiedBenchmarksMetaVersion
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r"""Dataset version."""
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class UnifiedBenchmarksMeta(BaseModel):
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as_of: str
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r"""ISO-8601 timestamp of when this data was last updated."""
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citation: Nullable[str]
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r"""Required attribution when republishing this data, or null when results span multiple sources (attribute each item individually by its `source` discriminator)."""
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model_count: int
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r"""Number of unique models in the response."""
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source: Annotated[
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Nullable[UnifiedBenchmarksMetaSource], PlainValidator(validate_open_enum(False))
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]
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r"""The source filter applied, or null when all sources are returned."""
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source_url: Nullable[str]
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r"""URL of the upstream data source, or null when results span multiple sources."""
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task_type: Nullable[str]
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r"""The task_type filter applied, or null if showing all."""
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version: UnifiedBenchmarksMetaVersion
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r"""Dataset version."""
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@model_serializer(mode="wrap")
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def serialize_model(self, handler):
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optional_fields = []
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nullable_fields = ["citation", "source", "source_url", "task_type"]
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null_default_fields = []
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serialized = handler(self)
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m = {}
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for n, f in type(self).model_fields.items():
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k = f.alias or n
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val = serialized.get(k)
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serialized.pop(k, None)
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optional_nullable = k in optional_fields and k in nullable_fields
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is_set = (
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self.__pydantic_fields_set__.intersection({n})
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or k in null_default_fields
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) # pylint: disable=no-member
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if val is not None and val != UNSET_SENTINEL:
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m[k] = val
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elif val != UNSET_SENTINEL and (
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not k in optional_fields or (optional_nullable and is_set)
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):
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m[k] = val
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return m
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