# 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, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from airflow_client.client.models.asset_alias_response import AssetAliasResponse
from airflow_client.client.models.asset_watcher_response import AssetWatcherResponse
from airflow_client.client.models.dag_schedule_asset_reference import DagScheduleAssetReference
from airflow_client.client.models.last_asset_event_response import LastAssetEventResponse
from airflow_client.client.models.task_inlet_asset_reference import TaskInletAssetReference
from airflow_client.client.models.task_outlet_asset_reference import TaskOutletAssetReference
from typing import Optional, Set
from typing_extensions import Self
from pydantic_core import to_jsonable_python

class AssetResponse(BaseModel):
    """
    Asset serializer for responses.
    """ # noqa: E501
    aliases: List[AssetAliasResponse]
    consuming_tasks: List[TaskInletAssetReference]
    created_at: datetime
    extra: Optional[Dict[str, Any]] = None
    group: StrictStr
    id: StrictInt
    last_asset_event: Optional[LastAssetEventResponse] = None
    name: StrictStr
    producing_tasks: List[TaskOutletAssetReference]
    scheduled_dags: List[DagScheduleAssetReference]
    updated_at: datetime
    uri: StrictStr
    watchers: List[AssetWatcherResponse]
    __properties: ClassVar[List[str]] = ["aliases", "consuming_tasks", "created_at", "extra", "group", "id", "last_asset_event", "name", "producing_tasks", "scheduled_dags", "updated_at", "uri", "watchers"]

    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 AssetResponse 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 aliases (list)
        _items = []
        if self.aliases:
            for _item_aliases in self.aliases:
                if _item_aliases:
                    _items.append(_item_aliases.to_dict())
            _dict['aliases'] = _items
        # override the default output from pydantic by calling `to_dict()` of each item in consuming_tasks (list)
        _items = []
        if self.consuming_tasks:
            for _item_consuming_tasks in self.consuming_tasks:
                if _item_consuming_tasks:
                    _items.append(_item_consuming_tasks.to_dict())
            _dict['consuming_tasks'] = _items
        # override the default output from pydantic by calling `to_dict()` of last_asset_event
        if self.last_asset_event:
            _dict['last_asset_event'] = self.last_asset_event.to_dict()
        # override the default output from pydantic by calling `to_dict()` of each item in producing_tasks (list)
        _items = []
        if self.producing_tasks:
            for _item_producing_tasks in self.producing_tasks:
                if _item_producing_tasks:
                    _items.append(_item_producing_tasks.to_dict())
            _dict['producing_tasks'] = _items
        # override the default output from pydantic by calling `to_dict()` of each item in scheduled_dags (list)
        _items = []
        if self.scheduled_dags:
            for _item_scheduled_dags in self.scheduled_dags:
                if _item_scheduled_dags:
                    _items.append(_item_scheduled_dags.to_dict())
            _dict['scheduled_dags'] = _items
        # override the default output from pydantic by calling `to_dict()` of each item in watchers (list)
        _items = []
        if self.watchers:
            for _item_watchers in self.watchers:
                if _item_watchers:
                    _items.append(_item_watchers.to_dict())
            _dict['watchers'] = _items
        return _dict

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

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

        _obj = cls.model_validate({
            "aliases": [AssetAliasResponse.from_dict(_item) for _item in obj["aliases"]] if obj.get("aliases") is not None else None,
            "consuming_tasks": [TaskInletAssetReference.from_dict(_item) for _item in obj["consuming_tasks"]] if obj.get("consuming_tasks") is not None else None,
            "created_at": obj.get("created_at"),
            "extra": obj.get("extra"),
            "group": obj.get("group"),
            "id": obj.get("id"),
            "last_asset_event": LastAssetEventResponse.from_dict(obj["last_asset_event"]) if obj.get("last_asset_event") is not None else None,
            "name": obj.get("name"),
            "producing_tasks": [TaskOutletAssetReference.from_dict(_item) for _item in obj["producing_tasks"]] if obj.get("producing_tasks") is not None else None,
            "scheduled_dags": [DagScheduleAssetReference.from_dict(_item) for _item in obj["scheduled_dags"]] if obj.get("scheduled_dags") is not None else None,
            "updated_at": obj.get("updated_at"),
            "uri": obj.get("uri"),
            "watchers": [AssetWatcherResponse.from_dict(_item) for _item in obj["watchers"]] if obj.get("watchers") is not None else None
        })
        return _obj


