"""Code generated by Speakeasy (https://speakeasy.com). DO NOT EDIT."""
# @generated-id: 3960bc4c545f

from __future__ import annotations
from .assistantmessage import AssistantMessage, AssistantMessageTypedDict
from .codeinterpretertool import CodeInterpreterTool, CodeInterpreterToolTypedDict
from .customconnector import CustomConnector, CustomConnectorTypedDict
from .documentlibrarytool import DocumentLibraryTool, DocumentLibraryToolTypedDict
from .guardrailconfig import GuardrailConfig, GuardrailConfigTypedDict
from .imagegenerationtool import ImageGenerationTool, ImageGenerationToolTypedDict
from .mistralpromptmode import MistralPromptMode
from .prediction import Prediction, PredictionTypedDict
from .reasoningeffort import ReasoningEffort
from .responseformat import ResponseFormat, ResponseFormatTypedDict
from .systemmessage import SystemMessage, SystemMessageTypedDict
from .tool import Tool, ToolTypedDict
from .toolchoice import ToolChoice, ToolChoiceTypedDict
from .toolchoiceenum import ToolChoiceEnum
from .toolmessage import ToolMessage, ToolMessageTypedDict
from .usermessage import UserMessage, UserMessageTypedDict
from .websearchpremiumtool import WebSearchPremiumTool, WebSearchPremiumToolTypedDict
from .websearchtool import WebSearchTool, WebSearchToolTypedDict
from mistralai.client.types import (
    BaseModel,
    Nullable,
    OptionalNullable,
    UNSET,
    UNSET_SENTINEL,
)
from mistralai.client.utils import get_discriminator
from pydantic import Discriminator, Tag, model_serializer
from typing import Any, Dict, List, Optional, Union
from typing_extensions import Annotated, NotRequired, TypeAliasType, TypedDict


AgentsCompletionRequestStopTypedDict = TypeAliasType(
    "AgentsCompletionRequestStopTypedDict", Union[str, List[str]]
)
r"""Stop generation if this token is detected. Or if one of these tokens is detected when providing an array"""


AgentsCompletionRequestStop = TypeAliasType(
    "AgentsCompletionRequestStop", Union[str, List[str]]
)
r"""Stop generation if this token is detected. Or if one of these tokens is detected when providing an array"""


AgentsCompletionRequestMessageTypedDict = TypeAliasType(
    "AgentsCompletionRequestMessageTypedDict",
    Union[
        SystemMessageTypedDict,
        UserMessageTypedDict,
        AssistantMessageTypedDict,
        ToolMessageTypedDict,
    ],
)


AgentsCompletionRequestMessage = Annotated[
    Union[
        Annotated[AssistantMessage, Tag("assistant")],
        Annotated[SystemMessage, Tag("system")],
        Annotated[ToolMessage, Tag("tool")],
        Annotated[UserMessage, Tag("user")],
    ],
    Discriminator(lambda m: get_discriminator(m, "role", "role")),
]


AgentsCompletionRequestToolTypedDict = TypeAliasType(
    "AgentsCompletionRequestToolTypedDict",
    Union[
        ToolTypedDict,
        WebSearchToolTypedDict,
        WebSearchPremiumToolTypedDict,
        CodeInterpreterToolTypedDict,
        ImageGenerationToolTypedDict,
        DocumentLibraryToolTypedDict,
        CustomConnectorTypedDict,
    ],
)


AgentsCompletionRequestTool = Union[
    Tool,
    WebSearchTool,
    WebSearchPremiumTool,
    CodeInterpreterTool,
    ImageGenerationTool,
    DocumentLibraryTool,
    CustomConnector,
]


AgentsCompletionRequestToolChoiceTypedDict = TypeAliasType(
    "AgentsCompletionRequestToolChoiceTypedDict",
    Union[ToolChoiceTypedDict, ToolChoiceEnum],
)


AgentsCompletionRequestToolChoice = TypeAliasType(
    "AgentsCompletionRequestToolChoice", Union[ToolChoice, ToolChoiceEnum]
)


class AgentsCompletionRequestTypedDict(TypedDict):
    messages: List[AgentsCompletionRequestMessageTypedDict]
    r"""The prompt(s) to generate completions for, encoded as a list of dict with role and content."""
    agent_id: str
    r"""The ID of the agent to use for this completion."""
    max_tokens: NotRequired[Nullable[int]]
    r"""The maximum number of tokens to generate in the completion. The token count of your prompt plus `max_tokens` cannot exceed the model's context length."""
    stream: NotRequired[bool]
    r"""Whether to stream back partial progress. If set, tokens will be sent as data-only server-side events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON."""
    stop: NotRequired[Nullable[AgentsCompletionRequestStopTypedDict]]
    r"""Stop generation if this token is detected. Or if one of these tokens is detected when providing an array"""
    random_seed: NotRequired[Nullable[int]]
    r"""The seed to use for random sampling. If set, different calls will generate deterministic results."""
    metadata: NotRequired[Nullable[Dict[str, Any]]]
    response_format: NotRequired[ResponseFormatTypedDict]
    r"""Specify the format that the model must output. By default it will use `{ \"type\": \"text\" }`. Setting to `{ \"type\": \"json_object\" }` enables JSON mode, which guarantees the message the model generates is in JSON. When using JSON mode you MUST also instruct the model to produce JSON yourself with a system or a user message. Setting to `{ \"type\": \"json_schema\" }` enables JSON schema mode, which guarantees the message the model generates is in JSON and follows the schema you provide."""
    tools: NotRequired[Nullable[List[AgentsCompletionRequestToolTypedDict]]]
    tool_choice: NotRequired[AgentsCompletionRequestToolChoiceTypedDict]
    presence_penalty: NotRequired[Nullable[float]]
    r"""The `presence_penalty` determines how much the model penalizes the repetition of words or phrases. A higher presence penalty encourages the model to use a wider variety of words and phrases, making the output more diverse and creative."""
    frequency_penalty: NotRequired[Nullable[float]]
    r"""The `frequency_penalty` penalizes the repetition of words based on their frequency in the generated text. A higher frequency penalty discourages the model from repeating words that have already appeared frequently in the output, promoting diversity and reducing repetition."""
    n: NotRequired[Nullable[int]]
    r"""Number of completions to return for each request, input tokens are only billed once."""
    prediction: NotRequired[PredictionTypedDict]
    r"""Enable users to specify an expected completion, optimizing response times by leveraging known or predictable content."""
    parallel_tool_calls: NotRequired[bool]
    reasoning_effort: NotRequired[Nullable[ReasoningEffort]]
    prompt_mode: NotRequired[Nullable[MistralPromptMode]]
    r"""Allows toggling between the reasoning mode and no system prompt. When set to `reasoning` the system prompt for reasoning models will be used."""
    guardrails: NotRequired[Nullable[List[GuardrailConfigTypedDict]]]


class AgentsCompletionRequest(BaseModel):
    messages: List[AgentsCompletionRequestMessage]
    r"""The prompt(s) to generate completions for, encoded as a list of dict with role and content."""

    agent_id: str
    r"""The ID of the agent to use for this completion."""

    max_tokens: OptionalNullable[int] = UNSET
    r"""The maximum number of tokens to generate in the completion. The token count of your prompt plus `max_tokens` cannot exceed the model's context length."""

    stream: Optional[bool] = False
    r"""Whether to stream back partial progress. If set, tokens will be sent as data-only server-side events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON."""

    stop: OptionalNullable[AgentsCompletionRequestStop] = UNSET
    r"""Stop generation if this token is detected. Or if one of these tokens is detected when providing an array"""

    random_seed: OptionalNullable[int] = UNSET
    r"""The seed to use for random sampling. If set, different calls will generate deterministic results."""

    metadata: OptionalNullable[Dict[str, Any]] = UNSET

    response_format: Optional[ResponseFormat] = None
    r"""Specify the format that the model must output. By default it will use `{ \"type\": \"text\" }`. Setting to `{ \"type\": \"json_object\" }` enables JSON mode, which guarantees the message the model generates is in JSON. When using JSON mode you MUST also instruct the model to produce JSON yourself with a system or a user message. Setting to `{ \"type\": \"json_schema\" }` enables JSON schema mode, which guarantees the message the model generates is in JSON and follows the schema you provide."""

    tools: OptionalNullable[List[AgentsCompletionRequestTool]] = UNSET

    tool_choice: Optional[AgentsCompletionRequestToolChoice] = None

    presence_penalty: OptionalNullable[float] = UNSET
    r"""The `presence_penalty` determines how much the model penalizes the repetition of words or phrases. A higher presence penalty encourages the model to use a wider variety of words and phrases, making the output more diverse and creative."""

    frequency_penalty: OptionalNullable[float] = UNSET
    r"""The `frequency_penalty` penalizes the repetition of words based on their frequency in the generated text. A higher frequency penalty discourages the model from repeating words that have already appeared frequently in the output, promoting diversity and reducing repetition."""

    n: OptionalNullable[int] = UNSET
    r"""Number of completions to return for each request, input tokens are only billed once."""

    prediction: Optional[Prediction] = None
    r"""Enable users to specify an expected completion, optimizing response times by leveraging known or predictable content."""

    parallel_tool_calls: Optional[bool] = None

    reasoning_effort: OptionalNullable[ReasoningEffort] = UNSET

    prompt_mode: OptionalNullable[MistralPromptMode] = UNSET
    r"""Allows toggling between the reasoning mode and no system prompt. When set to `reasoning` the system prompt for reasoning models will be used."""

    guardrails: OptionalNullable[List[GuardrailConfig]] = UNSET

    @model_serializer(mode="wrap")
    def serialize_model(self, handler):
        optional_fields = set(
            [
                "max_tokens",
                "stream",
                "stop",
                "random_seed",
                "metadata",
                "response_format",
                "tools",
                "tool_choice",
                "presence_penalty",
                "frequency_penalty",
                "n",
                "prediction",
                "parallel_tool_calls",
                "reasoning_effort",
                "prompt_mode",
                "guardrails",
            ]
        )
        nullable_fields = set(
            [
                "max_tokens",
                "stop",
                "random_seed",
                "metadata",
                "tools",
                "presence_penalty",
                "frequency_penalty",
                "n",
                "reasoning_effort",
                "prompt_mode",
                "guardrails",
            ]
        )
        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