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

from __future__ import annotations
from openrouter.types import (
    BaseModel,
    Nullable,
    OptionalNullable,
    UNSET,
    UNSET_SENTINEL,
    UnrecognizedStr,
)
from openrouter.utils import FieldMetadata, HeaderMetadata, QueryParamMetadata
import pydantic
from pydantic import model_serializer
from typing import Literal, Optional, Union
from typing_extensions import Annotated, NotRequired, TypedDict


class GetModelsGlobalsTypedDict(TypedDict):
    http_referer: NotRequired[str]
    r"""The app identifier should be your app's URL and is used as the primary identifier for rankings.
    This is used to track API usage per application.

    """
    x_open_router_title: NotRequired[str]
    r"""The app display name allows you to customize how your app appears in OpenRouter's dashboard.

    """
    x_open_router_categories: NotRequired[str]
    r"""Comma-separated list of app categories (e.g. \"cli-agent,cloud-agent\"). Used for marketplace rankings.

    """


class GetModelsGlobals(BaseModel):
    http_referer: Annotated[
        Optional[str],
        pydantic.Field(alias="HTTP-Referer"),
        FieldMetadata(header=HeaderMetadata(style="simple", explode=False)),
    ] = None
    r"""The app identifier should be your app's URL and is used as the primary identifier for rankings.
    This is used to track API usage per application.

    """

    x_open_router_title: Annotated[
        Optional[str],
        pydantic.Field(alias="X-OpenRouter-Title"),
        FieldMetadata(header=HeaderMetadata(style="simple", explode=False)),
    ] = None
    r"""The app display name allows you to customize how your app appears in OpenRouter's dashboard.

    """

    x_open_router_categories: Annotated[
        Optional[str],
        pydantic.Field(alias="X-OpenRouter-Categories"),
        FieldMetadata(header=HeaderMetadata(style="simple", explode=False)),
    ] = None
    r"""Comma-separated list of app categories (e.g. \"cli-agent,cloud-agent\"). Used for marketplace rankings.

    """

    @model_serializer(mode="wrap")
    def serialize_model(self, handler):
        optional_fields = set(
            ["HTTP-Referer", "X-OpenRouter-Title", "X-OpenRouter-Categories"]
        )
        serialized = handler(self)
        m = {}

        for n, f in type(self).model_fields.items():
            k = f.alias or n
            val = serialized.get(k, serialized.get(n))

            if val != UNSET_SENTINEL:
                if val is not None or k not in optional_fields:
                    m[k] = val

        return m


GetModelsCategory = Union[
    Literal[
        "programming",
        "roleplay",
        "marketing",
        "marketing/seo",
        "technology",
        "science",
        "translation",
        "legal",
        "finance",
        "health",
        "trivia",
        "academia",
    ],
    UnrecognizedStr,
]
r"""Filter models by use case category"""


GetModelsSort = Union[
    Literal[
        "most-popular",
        "newest",
        "top-weekly",
        "pricing-low-to-high",
        "pricing-high-to-low",
        "context-high-to-low",
        "throughput-high-to-low",
        "latency-low-to-high",
        "intelligence-high-to-low",
        "coding-high-to-low",
        "agentic-high-to-low",
        "design-arena-elo-high-to-low",
    ],
    UnrecognizedStr,
]
r"""Sort the returned models server-side. Prefer this over fetching the full list and sorting client-side. Options: pricing-low-to-high, pricing-high-to-low (average prompt/completion price), context-high-to-low (context length), throughput-high-to-low, latency-low-to-high (recent median performance), most-popular, top-weekly (tokens processed in the last week), newest (creation date), intelligence-high-to-low, coding-high-to-low, agentic-high-to-low (Artificial Analysis indices), design-arena-elo-high-to-low (best Design Arena ELO across arenas). Models without a score for the chosen benchmark are placed last. When omitted, the existing default ordering is preserved."""


Distillable = Union[
    Literal[
        "true",
        "false",
    ],
    UnrecognizedStr,
]
r"""Filter by distillation capability. \"true\" returns only distillable models, \"false\" excludes them."""


Zdr = Literal["true",]
r"""When set to \"true\", return only models with zero data retention endpoints."""


Region = Literal["eu",]
r"""Filter to models with endpoints in the given data region. Currently only \"eu\" is supported."""


class GetModelsRequestTypedDict(TypedDict):
    http_referer: NotRequired[str]
    r"""The app identifier should be your app's URL and is used as the primary identifier for rankings.
    This is used to track API usage per application.

    """
    x_open_router_title: NotRequired[str]
    r"""The app display name allows you to customize how your app appears in OpenRouter's dashboard.

    """
    x_open_router_categories: NotRequired[str]
    r"""Comma-separated list of app categories (e.g. \"cli-agent,cloud-agent\"). Used for marketplace rankings.

    """
    category: NotRequired[GetModelsCategory]
    r"""Filter models by use case category"""
    supported_parameters: NotRequired[str]
    r"""Filter models by supported parameter (comma-separated)"""
    output_modalities: NotRequired[str]
    r"""Filter models by output modality. Accepts a comma-separated list of modalities (text, image, audio, embeddings) or \"all\" to include all models. Defaults to \"text\"."""
    sort: NotRequired[GetModelsSort]
    r"""Sort the returned models server-side. Prefer this over fetching the full list and sorting client-side. Options: pricing-low-to-high, pricing-high-to-low (average prompt/completion price), context-high-to-low (context length), throughput-high-to-low, latency-low-to-high (recent median performance), most-popular, top-weekly (tokens processed in the last week), newest (creation date), intelligence-high-to-low, coding-high-to-low, agentic-high-to-low (Artificial Analysis indices), design-arena-elo-high-to-low (best Design Arena ELO across arenas). Models without a score for the chosen benchmark are placed last. When omitted, the existing default ordering is preserved."""
    q: NotRequired[str]
    r"""Free-text search by model name or slug."""
    input_modalities: NotRequired[str]
    r"""Filter models by input modality. Comma-separated list of: text, image, audio, file."""
    context: NotRequired[int]
    r"""Minimum context length (tokens). Models with smaller context are excluded."""
    min_price: NotRequired[Nullable[float]]
    r"""Minimum prompt price in $/M tokens."""
    max_price: NotRequired[Nullable[float]]
    r"""Maximum prompt price in $/M tokens."""
    arch: NotRequired[str]
    r"""Filter models by architecture/model family (e.g. GPT, Claude, Gemini, Llama)."""
    model_authors: NotRequired[str]
    r"""Filter models by the organization that created the model. Comma-separated list of author slugs."""
    providers: NotRequired[str]
    r"""Filter models by hosting provider. Comma-separated list of provider names."""
    distillable: NotRequired[Distillable]
    r"""Filter by distillation capability. \"true\" returns only distillable models, \"false\" excludes them."""
    zdr: NotRequired[Zdr]
    r"""When set to \"true\", return only models with zero data retention endpoints."""
    region: NotRequired[Region]
    r"""Filter to models with endpoints in the given data region. Currently only \"eu\" is supported."""
    min_output_price: NotRequired[Nullable[float]]
    r"""Minimum completion (output) price in $/M tokens."""
    max_output_price: NotRequired[Nullable[float]]
    r"""Maximum completion (output) price in $/M tokens."""
    min_age_days: NotRequired[Nullable[int]]
    r"""Minimum model age in days since its creation date."""
    max_age_days: NotRequired[Nullable[int]]
    r"""Maximum model age in days since its creation date."""
    min_intelligence_index: NotRequired[Nullable[float]]
    r"""Minimum Artificial Analysis intelligence index."""
    max_intelligence_index: NotRequired[Nullable[float]]
    r"""Maximum Artificial Analysis intelligence index."""
    min_coding_index: NotRequired[Nullable[float]]
    r"""Minimum Artificial Analysis coding index."""
    max_coding_index: NotRequired[Nullable[float]]
    r"""Maximum Artificial Analysis coding index."""
    min_agentic_index: NotRequired[Nullable[float]]
    r"""Minimum Artificial Analysis agentic index."""
    max_agentic_index: NotRequired[Nullable[float]]
    r"""Maximum Artificial Analysis agentic index."""
    min_tool_success_rate: NotRequired[Nullable[float]]
    r"""Minimum tool-calling success rate, as a fraction in [0, 1] (e.g. 0.9 = 90% of requests finishing with a tool_calls finish reason)."""
    max_tool_success_rate: NotRequired[Nullable[float]]
    r"""Maximum tool-calling success rate, as a fraction in [0, 1]."""


class GetModelsRequest(BaseModel):
    http_referer: Annotated[
        Optional[str],
        pydantic.Field(alias="HTTP-Referer"),
        FieldMetadata(header=HeaderMetadata(style="simple", explode=False)),
    ] = None
    r"""The app identifier should be your app's URL and is used as the primary identifier for rankings.
    This is used to track API usage per application.

    """

    x_open_router_title: Annotated[
        Optional[str],
        pydantic.Field(alias="X-OpenRouter-Title"),
        FieldMetadata(header=HeaderMetadata(style="simple", explode=False)),
    ] = None
    r"""The app display name allows you to customize how your app appears in OpenRouter's dashboard.

    """

    x_open_router_categories: Annotated[
        Optional[str],
        pydantic.Field(alias="X-OpenRouter-Categories"),
        FieldMetadata(header=HeaderMetadata(style="simple", explode=False)),
    ] = None
    r"""Comma-separated list of app categories (e.g. \"cli-agent,cloud-agent\"). Used for marketplace rankings.

    """

    category: Annotated[
        Optional[GetModelsCategory],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Filter models by use case category"""

    supported_parameters: Annotated[
        Optional[str],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Filter models by supported parameter (comma-separated)"""

    output_modalities: Annotated[
        Optional[str],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Filter models by output modality. Accepts a comma-separated list of modalities (text, image, audio, embeddings) or \"all\" to include all models. Defaults to \"text\"."""

    sort: Annotated[
        Optional[GetModelsSort],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Sort the returned models server-side. Prefer this over fetching the full list and sorting client-side. Options: pricing-low-to-high, pricing-high-to-low (average prompt/completion price), context-high-to-low (context length), throughput-high-to-low, latency-low-to-high (recent median performance), most-popular, top-weekly (tokens processed in the last week), newest (creation date), intelligence-high-to-low, coding-high-to-low, agentic-high-to-low (Artificial Analysis indices), design-arena-elo-high-to-low (best Design Arena ELO across arenas). Models without a score for the chosen benchmark are placed last. When omitted, the existing default ordering is preserved."""

    q: Annotated[
        Optional[str],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Free-text search by model name or slug."""

    input_modalities: Annotated[
        Optional[str],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Filter models by input modality. Comma-separated list of: text, image, audio, file."""

    context: Annotated[
        Optional[int],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Minimum context length (tokens). Models with smaller context are excluded."""

    min_price: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Minimum prompt price in $/M tokens."""

    max_price: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Maximum prompt price in $/M tokens."""

    arch: Annotated[
        Optional[str],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Filter models by architecture/model family (e.g. GPT, Claude, Gemini, Llama)."""

    model_authors: Annotated[
        Optional[str],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Filter models by the organization that created the model. Comma-separated list of author slugs."""

    providers: Annotated[
        Optional[str],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Filter models by hosting provider. Comma-separated list of provider names."""

    distillable: Annotated[
        Optional[Distillable],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Filter by distillation capability. \"true\" returns only distillable models, \"false\" excludes them."""

    zdr: Annotated[
        Optional[Zdr],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""When set to \"true\", return only models with zero data retention endpoints."""

    region: Annotated[
        Optional[Region],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = None
    r"""Filter to models with endpoints in the given data region. Currently only \"eu\" is supported."""

    min_output_price: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Minimum completion (output) price in $/M tokens."""

    max_output_price: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Maximum completion (output) price in $/M tokens."""

    min_age_days: Annotated[
        OptionalNullable[int],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Minimum model age in days since its creation date."""

    max_age_days: Annotated[
        OptionalNullable[int],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Maximum model age in days since its creation date."""

    min_intelligence_index: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Minimum Artificial Analysis intelligence index."""

    max_intelligence_index: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Maximum Artificial Analysis intelligence index."""

    min_coding_index: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Minimum Artificial Analysis coding index."""

    max_coding_index: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Maximum Artificial Analysis coding index."""

    min_agentic_index: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Minimum Artificial Analysis agentic index."""

    max_agentic_index: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Maximum Artificial Analysis agentic index."""

    min_tool_success_rate: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Minimum tool-calling success rate, as a fraction in [0, 1] (e.g. 0.9 = 90% of requests finishing with a tool_calls finish reason)."""

    max_tool_success_rate: Annotated[
        OptionalNullable[float],
        FieldMetadata(query=QueryParamMetadata(style="form", explode=True)),
    ] = UNSET
    r"""Maximum tool-calling success rate, as a fraction in [0, 1]."""

    @model_serializer(mode="wrap")
    def serialize_model(self, handler):
        optional_fields = set(
            [
                "HTTP-Referer",
                "X-OpenRouter-Title",
                "X-OpenRouter-Categories",
                "category",
                "supported_parameters",
                "output_modalities",
                "sort",
                "q",
                "input_modalities",
                "context",
                "min_price",
                "max_price",
                "arch",
                "model_authors",
                "providers",
                "distillable",
                "zdr",
                "region",
                "min_output_price",
                "max_output_price",
                "min_age_days",
                "max_age_days",
                "min_intelligence_index",
                "max_intelligence_index",
                "min_coding_index",
                "max_coding_index",
                "min_agentic_index",
                "max_agentic_index",
                "min_tool_success_rate",
                "max_tool_success_rate",
            ]
        )
        nullable_fields = set(
            [
                "min_price",
                "max_price",
                "min_output_price",
                "max_output_price",
                "min_age_days",
                "max_age_days",
                "min_intelligence_index",
                "max_intelligence_index",
                "min_coding_index",
                "max_coding_index",
                "min_agentic_index",
                "max_agentic_index",
                "min_tool_success_rate",
                "max_tool_success_rate",
            ]
        )
        serialized = handler(self)
        m = {}

        for n, f in type(self).model_fields.items():
            k = f.alias or n
            val = serialized.get(k, serialized.get(n))
            is_nullable_and_explicitly_set = (
                k in nullable_fields
                and (self.__pydantic_fields_set__.intersection({n}))  # pylint: disable=no-member
            )

            if val != UNSET_SENTINEL:
                if (
                    val is not None
                    or k not in optional_fields
                    or is_nullable_and_explicitly_set
                ):
                    m[k] = val

        return m