airflow_client/client/models/task_instance_collection_response.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, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from airflow_client.client.models.task_instance_response import TaskInstanceResponse
from typing import Optional, Set
from typing_extensions import Self
from pydantic_core import to_jsonable_python

class TaskInstanceCollectionResponse(BaseModel):
    """
    Task instance collection response supporting both offset and cursor pagination.  A single flat model is used instead of a discriminated union (``Annotated[Offset | Cursor, Field(discriminator=...)]``) because the OpenAPI ``oneOf`` + ``discriminator`` construct is not handled correctly by ``@hey-api/openapi-ts`` / ``@7nohe/openapi-react-query-codegen``: return types degrade to ``unknown`` in JSDoc and can produce incorrect TypeScript types (see hey-api/openapi-ts#1613, #3270).
    """ # noqa: E501
    next_cursor: Optional[StrictStr] = None
    previous_cursor: Optional[StrictStr] = None
    task_instances: List[TaskInstanceResponse]
    total_entries: Optional[StrictInt] = None
    total_entries_limit: Optional[StrictInt] = None
    __properties: ClassVar[List[str]] = ["next_cursor", "previous_cursor", "task_instances", "total_entries", "total_entries_limit"]

    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 TaskInstanceCollectionResponse 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,
        )
        # override the default output from pydantic by calling `to_dict()` of each item in task_instances (list)
        _items = []
        if self.task_instances:
            for _item_task_instances in self.task_instances:
                _items.append(_item_task_instances.to_dict() if _item_task_instances is not None else None)
            _dict['task_instances'] = _items
        return _dict

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

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

        _obj = cls.model_validate({
            "next_cursor": obj.get("next_cursor"),
            "previous_cursor": obj.get("previous_cursor"),
            "task_instances": [TaskInstanceResponse.from_dict(_item) for _item in obj["task_instances"]] if obj.get("task_instances") is not None else None,
            "total_entries": obj.get("total_entries"),
            "total_entries_limit": obj.get("total_entries_limit")
        })
        return _obj