"""Code generated by Speakeasy (https://speakeasy.com). DO NOT EDIT."""
# @generated-id: c6a65acdd1a2

from __future__ import annotations
from mistralai.client.types import (
    BaseModel,
    Nullable,
    OptionalNullable,
    UNSET,
    UNSET_SENTINEL,
)
from pydantic import model_serializer
from typing_extensions import NotRequired, TypedDict


class MetricTypedDict(TypedDict):
    r"""Metrics at the step number during the fine-tuning job. Use these metrics to assess if the training is going smoothly (loss should decrease, token accuracy should increase)."""

    train_loss: NotRequired[Nullable[float]]
    valid_loss: NotRequired[Nullable[float]]
    valid_mean_token_accuracy: NotRequired[Nullable[float]]


class Metric(BaseModel):
    r"""Metrics at the step number during the fine-tuning job. Use these metrics to assess if the training is going smoothly (loss should decrease, token accuracy should increase)."""

    train_loss: OptionalNullable[float] = UNSET

    valid_loss: OptionalNullable[float] = UNSET

    valid_mean_token_accuracy: OptionalNullable[float] = UNSET

    @model_serializer(mode="wrap")
    def serialize_model(self, handler):
        optional_fields = set(["train_loss", "valid_loss", "valid_mean_token_accuracy"])
        nullable_fields = set(["train_loss", "valid_loss", "valid_mean_token_accuracy"])
        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
