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

from __future__ import annotations
from mistralai.types import BaseModel, Nullable, OptionalNullable, UNSET, UNSET_SENTINEL
from mistralai.utils import validate_const
import pydantic
from pydantic import model_serializer
from pydantic.functional_validators import AfterValidator
from typing import Literal, Optional
from typing_extensions import Annotated, NotRequired, TypedDict


LegacyJobMetadataOutObject = Literal["job.metadata"]


class LegacyJobMetadataOutTypedDict(TypedDict):
    details: str
    expected_duration_seconds: NotRequired[Nullable[int]]
    r"""The approximated time (in seconds) for the fine-tuning process to complete."""
    cost: NotRequired[Nullable[float]]
    r"""The cost of the fine-tuning job."""
    cost_currency: NotRequired[Nullable[str]]
    r"""The currency used for the fine-tuning job cost."""
    train_tokens_per_step: NotRequired[Nullable[int]]
    r"""The number of tokens consumed by one training step."""
    train_tokens: NotRequired[Nullable[int]]
    r"""The total number of tokens used during the fine-tuning process."""
    data_tokens: NotRequired[Nullable[int]]
    r"""The total number of tokens in the training dataset."""
    estimated_start_time: NotRequired[Nullable[int]]
    deprecated: NotRequired[bool]
    epochs: NotRequired[Nullable[float]]
    r"""The number of complete passes through the entire training dataset."""
    training_steps: NotRequired[Nullable[int]]
    r"""The number of training steps to perform. A training step refers to a single update of the model weights during the fine-tuning process. This update is typically calculated using a batch of samples from the training dataset."""
    object: LegacyJobMetadataOutObject


class LegacyJobMetadataOut(BaseModel):
    details: str

    expected_duration_seconds: OptionalNullable[int] = UNSET
    r"""The approximated time (in seconds) for the fine-tuning process to complete."""

    cost: OptionalNullable[float] = UNSET
    r"""The cost of the fine-tuning job."""

    cost_currency: OptionalNullable[str] = UNSET
    r"""The currency used for the fine-tuning job cost."""

    train_tokens_per_step: OptionalNullable[int] = UNSET
    r"""The number of tokens consumed by one training step."""

    train_tokens: OptionalNullable[int] = UNSET
    r"""The total number of tokens used during the fine-tuning process."""

    data_tokens: OptionalNullable[int] = UNSET
    r"""The total number of tokens in the training dataset."""

    estimated_start_time: OptionalNullable[int] = UNSET

    deprecated: Optional[bool] = True

    epochs: OptionalNullable[float] = UNSET
    r"""The number of complete passes through the entire training dataset."""

    training_steps: OptionalNullable[int] = UNSET
    r"""The number of training steps to perform. A training step refers to a single update of the model weights during the fine-tuning process. This update is typically calculated using a batch of samples from the training dataset."""

    OBJECT: Annotated[
        Annotated[
            Optional[LegacyJobMetadataOutObject],
            AfterValidator(validate_const("job.metadata")),
        ],
        pydantic.Field(alias="object"),
    ] = "job.metadata"

    @model_serializer(mode="wrap")
    def serialize_model(self, handler):
        optional_fields = [
            "expected_duration_seconds",
            "cost",
            "cost_currency",
            "train_tokens_per_step",
            "train_tokens",
            "data_tokens",
            "estimated_start_time",
            "deprecated",
            "epochs",
            "training_steps",
            "object",
        ]
        nullable_fields = [
            "expected_duration_seconds",
            "cost",
            "cost_currency",
            "train_tokens_per_step",
            "train_tokens",
            "data_tokens",
            "estimated_start_time",
            "epochs",
            "training_steps",
        ]
        null_default_fields = []

        serialized = handler(self)

        m = {}

        for n, f in 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