# 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, Field, StrictBool, StrictFloat, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional, Union
from airflow_client.client.models.dag_run_type import DagRunType
from airflow_client.client.models.dag_tag_response import DagTagResponse
from typing import Optional, Set
from typing_extensions import Self
from pydantic_core import to_jsonable_python

class DAGResponse(BaseModel):
    """
    Dag serializer for responses.
    """ # noqa: E501
    allowed_run_types: Optional[List[DagRunType]] = None
    bundle_name: Optional[StrictStr] = None
    bundle_version: Optional[StrictStr] = None
    dag_display_name: StrictStr
    dag_id: StrictStr
    description: Optional[StrictStr] = None
    file_token: StrictStr = Field(description="Return file token.")
    fileloc: StrictStr
    has_import_errors: StrictBool
    has_task_concurrency_limits: StrictBool
    is_backfillable: StrictBool = Field(description="Whether this Dag's schedule supports backfilling.")
    is_paused: StrictBool
    is_stale: StrictBool
    last_expired: Optional[datetime] = None
    last_parse_duration: Optional[Union[StrictFloat, StrictInt]] = None
    last_parsed_time: Optional[datetime] = None
    max_active_runs: Optional[StrictInt] = None
    max_active_tasks: StrictInt
    max_consecutive_failed_dag_runs: StrictInt
    next_dagrun_data_interval_end: Optional[datetime] = None
    next_dagrun_data_interval_start: Optional[datetime] = None
    next_dagrun_logical_date: Optional[datetime] = None
    next_dagrun_run_after: Optional[datetime] = None
    owners: List[StrictStr]
    relative_fileloc: Optional[StrictStr] = None
    tags: List[DagTagResponse]
    timetable_description: Optional[StrictStr] = None
    timetable_partitioned: StrictBool
    timetable_periodic: StrictBool
    timetable_summary: Optional[StrictStr] = None
    __properties: ClassVar[List[str]] = ["allowed_run_types", "bundle_name", "bundle_version", "dag_display_name", "dag_id", "description", "file_token", "fileloc", "has_import_errors", "has_task_concurrency_limits", "is_backfillable", "is_paused", "is_stale", "last_expired", "last_parse_duration", "last_parsed_time", "max_active_runs", "max_active_tasks", "max_consecutive_failed_dag_runs", "next_dagrun_data_interval_end", "next_dagrun_data_interval_start", "next_dagrun_logical_date", "next_dagrun_run_after", "owners", "relative_fileloc", "tags", "timetable_description", "timetable_partitioned", "timetable_periodic", "timetable_summary"]

    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 DAGResponse 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.
        * OpenAPI `readOnly` fields are excluded.
        * OpenAPI `readOnly` fields are excluded.
        """
        excluded_fields: Set[str] = set([
            "file_token",
            "is_backfillable",
        ])

        _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 tags (list)
        _items = []
        if self.tags:
            for _item_tags in self.tags:
                if _item_tags:
                    _items.append(_item_tags.to_dict())
            _dict['tags'] = _items
        return _dict

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

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

        _obj = cls.model_validate({
            "allowed_run_types": obj.get("allowed_run_types"),
            "bundle_name": obj.get("bundle_name"),
            "bundle_version": obj.get("bundle_version"),
            "dag_display_name": obj.get("dag_display_name"),
            "dag_id": obj.get("dag_id"),
            "description": obj.get("description"),
            "file_token": obj.get("file_token"),
            "fileloc": obj.get("fileloc"),
            "has_import_errors": obj.get("has_import_errors"),
            "has_task_concurrency_limits": obj.get("has_task_concurrency_limits"),
            "is_backfillable": obj.get("is_backfillable"),
            "is_paused": obj.get("is_paused"),
            "is_stale": obj.get("is_stale"),
            "last_expired": obj.get("last_expired"),
            "last_parse_duration": obj.get("last_parse_duration"),
            "last_parsed_time": obj.get("last_parsed_time"),
            "max_active_runs": obj.get("max_active_runs"),
            "max_active_tasks": obj.get("max_active_tasks"),
            "max_consecutive_failed_dag_runs": obj.get("max_consecutive_failed_dag_runs"),
            "next_dagrun_data_interval_end": obj.get("next_dagrun_data_interval_end"),
            "next_dagrun_data_interval_start": obj.get("next_dagrun_data_interval_start"),
            "next_dagrun_logical_date": obj.get("next_dagrun_logical_date"),
            "next_dagrun_run_after": obj.get("next_dagrun_run_after"),
            "owners": obj.get("owners"),
            "relative_fileloc": obj.get("relative_fileloc"),
            "tags": [DagTagResponse.from_dict(_item) for _item in obj["tags"]] if obj.get("tags") is not None else None,
            "timetable_description": obj.get("timetable_description"),
            "timetable_partitioned": obj.get("timetable_partitioned"),
            "timetable_periodic": obj.get("timetable_periodic"),
            "timetable_summary": obj.get("timetable_summary")
        })
        return _obj


