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https://github.com/Ladebeze66/llm_ticket3.git
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129 lines
4.7 KiB
Python
129 lines
4.7 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 mistralai.types import BaseModel, Nullable, OptionalNullable, UNSET, UNSET_SENTINEL
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from mistralai.utils import validate_const
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import pydantic
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from pydantic import model_serializer
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from pydantic.functional_validators import AfterValidator
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from typing import Literal, Optional
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from typing_extensions import Annotated, NotRequired, TypedDict
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LegacyJobMetadataOutObject = Literal["job.metadata"]
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class LegacyJobMetadataOutTypedDict(TypedDict):
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details: str
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expected_duration_seconds: NotRequired[Nullable[int]]
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r"""The approximated time (in seconds) for the fine-tuning process to complete."""
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cost: NotRequired[Nullable[float]]
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r"""The cost of the fine-tuning job."""
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cost_currency: NotRequired[Nullable[str]]
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r"""The currency used for the fine-tuning job cost."""
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train_tokens_per_step: NotRequired[Nullable[int]]
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r"""The number of tokens consumed by one training step."""
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train_tokens: NotRequired[Nullable[int]]
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r"""The total number of tokens used during the fine-tuning process."""
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data_tokens: NotRequired[Nullable[int]]
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r"""The total number of tokens in the training dataset."""
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estimated_start_time: NotRequired[Nullable[int]]
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deprecated: NotRequired[bool]
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epochs: NotRequired[Nullable[float]]
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r"""The number of complete passes through the entire training dataset."""
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training_steps: NotRequired[Nullable[int]]
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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."""
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object: LegacyJobMetadataOutObject
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class LegacyJobMetadataOut(BaseModel):
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details: str
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expected_duration_seconds: OptionalNullable[int] = UNSET
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r"""The approximated time (in seconds) for the fine-tuning process to complete."""
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cost: OptionalNullable[float] = UNSET
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r"""The cost of the fine-tuning job."""
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cost_currency: OptionalNullable[str] = UNSET
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r"""The currency used for the fine-tuning job cost."""
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train_tokens_per_step: OptionalNullable[int] = UNSET
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r"""The number of tokens consumed by one training step."""
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train_tokens: OptionalNullable[int] = UNSET
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r"""The total number of tokens used during the fine-tuning process."""
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data_tokens: OptionalNullable[int] = UNSET
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r"""The total number of tokens in the training dataset."""
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estimated_start_time: OptionalNullable[int] = UNSET
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deprecated: Optional[bool] = True
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epochs: OptionalNullable[float] = UNSET
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r"""The number of complete passes through the entire training dataset."""
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training_steps: OptionalNullable[int] = UNSET
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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."""
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OBJECT: Annotated[
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Annotated[
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Optional[LegacyJobMetadataOutObject],
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AfterValidator(validate_const("job.metadata")),
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],
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pydantic.Field(alias="object"),
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] = "job.metadata"
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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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"expected_duration_seconds",
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"cost",
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"cost_currency",
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"train_tokens_per_step",
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"train_tokens",
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"data_tokens",
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"estimated_start_time",
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"deprecated",
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"epochs",
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"training_steps",
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"object",
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]
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nullable_fields = [
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"expected_duration_seconds",
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"cost",
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"cost_currency",
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"train_tokens_per_step",
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"train_tokens",
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"data_tokens",
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"estimated_start_time",
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"epochs",
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"training_steps",
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]
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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 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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