# 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, StrictFloat, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional, Union
from uuid import UUID
from airflow_client.client.models.dag_version_response import DagVersionResponse
from airflow_client.client.models.job_response import JobResponse
from airflow_client.client.models.task_instance_state import TaskInstanceState
from airflow_client.client.models.trigger_response import TriggerResponse
from typing import Optional, Set
from typing_extensions import Self
from pydantic_core import to_jsonable_python

class TaskInstanceResponse(BaseModel):
    """
    TaskInstance serializer for responses.
    """ # noqa: E501
    dag_display_name: StrictStr
    dag_id: StrictStr
    dag_run_id: StrictStr
    dag_version: Optional[DagVersionResponse] = None
    duration: Optional[Union[StrictFloat, StrictInt]] = None
    end_date: Optional[datetime] = None
    executor: Optional[StrictStr] = None
    executor_config: StrictStr
    hostname: Optional[StrictStr] = None
    id: UUID
    logical_date: Optional[datetime] = None
    map_index: StrictInt
    max_tries: StrictInt
    note: Optional[StrictStr] = None
    operator: Optional[StrictStr] = None
    operator_name: Optional[StrictStr] = None
    pid: Optional[StrictInt] = None
    pool: StrictStr
    pool_slots: StrictInt
    priority_weight: Optional[StrictInt] = None
    queue: Optional[StrictStr] = None
    queued_when: Optional[datetime] = None
    rendered_fields: Optional[Dict[str, Any]] = None
    rendered_map_index: Optional[StrictStr] = None
    run_after: datetime
    scheduled_when: Optional[datetime] = None
    start_date: Optional[datetime] = None
    state: Optional[TaskInstanceState] = None
    task_display_name: StrictStr
    task_id: StrictStr
    trigger: Optional[TriggerResponse] = None
    triggerer_job: Optional[JobResponse] = None
    try_number: StrictInt
    unixname: Optional[StrictStr] = None
    __properties: ClassVar[List[str]] = ["dag_display_name", "dag_id", "dag_run_id", "dag_version", "duration", "end_date", "executor", "executor_config", "hostname", "id", "logical_date", "map_index", "max_tries", "note", "operator", "operator_name", "pid", "pool", "pool_slots", "priority_weight", "queue", "queued_when", "rendered_fields", "rendered_map_index", "run_after", "scheduled_when", "start_date", "state", "task_display_name", "task_id", "trigger", "triggerer_job", "try_number", "unixname"]

    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 TaskInstanceResponse 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 dag_version
        if self.dag_version:
            _dict['dag_version'] = self.dag_version.to_dict()
        # override the default output from pydantic by calling `to_dict()` of trigger
        if self.trigger:
            _dict['trigger'] = self.trigger.to_dict()
        # override the default output from pydantic by calling `to_dict()` of triggerer_job
        if self.triggerer_job:
            _dict['triggerer_job'] = self.triggerer_job.to_dict()
        return _dict

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

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

        _obj = cls.model_validate({
            "dag_display_name": obj.get("dag_display_name"),
            "dag_id": obj.get("dag_id"),
            "dag_run_id": obj.get("dag_run_id"),
            "dag_version": DagVersionResponse.from_dict(obj["dag_version"]) if obj.get("dag_version") is not None else None,
            "duration": obj.get("duration"),
            "end_date": obj.get("end_date"),
            "executor": obj.get("executor"),
            "executor_config": obj.get("executor_config"),
            "hostname": obj.get("hostname"),
            "id": obj.get("id"),
            "logical_date": obj.get("logical_date"),
            "map_index": obj.get("map_index"),
            "max_tries": obj.get("max_tries"),
            "note": obj.get("note"),
            "operator": obj.get("operator"),
            "operator_name": obj.get("operator_name"),
            "pid": obj.get("pid"),
            "pool": obj.get("pool"),
            "pool_slots": obj.get("pool_slots"),
            "priority_weight": obj.get("priority_weight"),
            "queue": obj.get("queue"),
            "queued_when": obj.get("queued_when"),
            "rendered_fields": obj.get("rendered_fields"),
            "rendered_map_index": obj.get("rendered_map_index"),
            "run_after": obj.get("run_after"),
            "scheduled_when": obj.get("scheduled_when"),
            "start_date": obj.get("start_date"),
            "state": obj.get("state"),
            "task_display_name": obj.get("task_display_name"),
            "task_id": obj.get("task_id"),
            "trigger": TriggerResponse.from_dict(obj["trigger"]) if obj.get("trigger") is not None else None,
            "triggerer_job": JobResponse.from_dict(obj["triggerer_job"]) if obj.get("triggerer_job") is not None else None,
            "try_number": obj.get("try_number"),
            "unixname": obj.get("unixname")
        })
        return _obj


