# 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 datetime import datetime
from pydantic import BaseModel, ConfigDict, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from typing import Optional, Set
from typing_extensions import Self
from pydantic_core import to_jsonable_python

class DagRunAssetReference(BaseModel):
    """
    DagRun serializer for asset responses.
    """ # noqa: E501
    dag_id: StrictStr
    data_interval_end: Optional[datetime] = None
    data_interval_start: Optional[datetime] = None
    end_date: Optional[datetime] = None
    logical_date: Optional[datetime] = None
    partition_key: Optional[StrictStr] = None
    run_id: StrictStr
    start_date: datetime
    state: StrictStr
    __properties: ClassVar[List[str]] = ["dag_id", "data_interval_end", "data_interval_start", "end_date", "logical_date", "partition_key", "run_id", "start_date", "state"]

    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 DagRunAssetReference 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 DagRunAssetReference from a dict"""
        if obj is None:
            return None

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

        _obj = cls.model_validate({
            "dag_id": obj.get("dag_id"),
            "data_interval_end": obj.get("data_interval_end"),
            "data_interval_start": obj.get("data_interval_start"),
            "end_date": obj.get("end_date"),
            "logical_date": obj.get("logical_date"),
            "partition_key": obj.get("partition_key"),
            "run_id": obj.get("run_id"),
            "start_date": obj.get("start_date"),
            "state": obj.get("state")
        })
        return _obj


