airflow_client/client/models/patch_task_instance_body.py

# coding: utf-8

"""
    Airflow API

    Airflow API. All endpoints located under ``/api/v2`` can be used safely, are stable and backward compatible. Endpoints located under ``/ui`` are dedicated to the UI and are subject to breaking change depending on the need of the frontend. Users should not rely on those but use the public ones instead.  **Filtering with pattern parameters.** Many list endpoints accept ``*_pattern`` and ``*_prefix_pattern`` query parameters. Unless a parameter's own description says otherwise, ``*_pattern`` is a case-insensitive substring match (SQL ``ILIKE '%term%'``) where ``%`` matches any sequence and ``_`` matches any single character (e.g. ``%customer_%``) — convenient, but it cannot use B-tree indexes, so it is slow on large tables. ``*_prefix_pattern`` matches the start of the value, is case-sensitive and index-friendly (prefer it at scale); there ``%`` and ``_`` are literal and trailing non-alphanumeric characters are stripped so the range scan stays index-compatible under locale-aware collations (e.g. ``test_`` matches values starting with ``test``, and ``s3://`` matches ``s3``). In both, ``|`` means OR (e.g. ``dag1|dag2``) and ``~`` matches everything. Regular expressions are not supported by these parameters; regex-capable endpoints expose a separate parameter.

    The version of the OpenAPI document: 2
    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
"""  # noqa: E501


from __future__ import annotations
import pprint
import re  # noqa: F401
import json

from pydantic import BaseModel, ConfigDict, Field, StrictBool
from typing import Any, ClassVar, Dict, List, Optional
from typing_extensions import Annotated
from airflow_client.client.models.task_instance_state import TaskInstanceState
from typing import Optional, Set
from typing_extensions import Self
from pydantic_core import to_jsonable_python

class PatchTaskInstanceBody(BaseModel):
    """
    Request body for patching task instance state.
    """ # noqa: E501
    include_downstream: Optional[StrictBool] = False
    include_future: Optional[StrictBool] = False
    include_past: Optional[StrictBool] = False
    include_upstream: Optional[StrictBool] = False
    new_state: Optional[TaskInstanceState] = None
    note: Optional[Annotated[str, Field(strict=True, max_length=1000)]] = None
    __properties: ClassVar[List[str]] = ["include_downstream", "include_future", "include_past", "include_upstream", "new_state", "note"]

    model_config = ConfigDict(
        validate_by_name=True,
        validate_by_alias=True,
        validate_assignment=True,
        protected_namespaces=(),
    )


    def to_str(self) -> str:
        """Returns the string representation of the model using alias"""
        return pprint.pformat(self.model_dump(by_alias=True))

    def to_json(self) -> str:
        """Returns the JSON representation of the model using alias"""
        return json.dumps(to_jsonable_python(self.to_dict()))

    @classmethod
    def from_json(cls, json_str: str) -> Optional[Self]:
        """Create an instance of PatchTaskInstanceBody from a JSON string"""
        return cls.from_dict(json.loads(json_str))

    def to_dict(self) -> Dict[str, Any]:
        """Return the dictionary representation of the model using alias.

        This has the following differences from calling pydantic's
        `self.model_dump(by_alias=True)`:

        * `None` is only added to the output dict for nullable fields that
          were set at model initialization. Other fields with value `None`
          are ignored.
        """
        excluded_fields: Set[str] = set([
        ])

        _dict = self.model_dump(
            by_alias=True,
            exclude=excluded_fields,
            exclude_none=True,
        )
        return _dict

    @classmethod
    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
        """Create an instance of PatchTaskInstanceBody from a dict"""
        if obj is None:
            return None

        if not isinstance(obj, dict):
            return cls.model_validate(obj)

        _obj = cls.model_validate({
            "include_downstream": obj.get("include_downstream") if obj.get("include_downstream") is not None else False,
            "include_future": obj.get("include_future") if obj.get("include_future") is not None else False,
            "include_past": obj.get("include_past") if obj.get("include_past") is not None else False,
            "include_upstream": obj.get("include_upstream") if obj.get("include_upstream") is not None else False,
            "new_state": obj.get("new_state"),
            "note": obj.get("note")
        })
        return _obj