Source code for openapi_client.models.adhoc_trigger_job_request

# coding: utf-8

"""
    Amorphic Data Platform

    Amorphic Data Platform - API Definition documentation

    The version of the OpenAPI document: 1.0
    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, Field, StrictBool, StrictInt, StrictStr, field_validator
from typing import Any, ClassVar, Dict, List, Optional
from typing import Optional, Set
from typing_extensions import Self

[docs] class AdhocTriggerJobRequest(BaseModel): """ AdhocTriggerJobRequest """ # noqa: E501 number_of_workers: Optional[StrictInt] = Field(default=None, description="The number of worker nodes used to run data profiling job", alias="NumberOfWorkers") timeout: Optional[StrictInt] = Field(default=None, description="Timeout of the data profiling job", alias="Timeout") file_processing_mode: Optional[StrictStr] = Field(default=None, description="The file processing mode for running BDA extraction Job", alias="FileProcessingMode") file_ids: Optional[List[StrictStr]] = Field(default=None, description="List of specific file IDs to process (used with file processing mode)", alias="FileIds") from_time: Optional[StrictStr] = Field(default=None, description="Start time for time-based processing (ISO 8601 format)", alias="FromTime") to_time: Optional[StrictStr] = Field(default=None, description="End time for time-based processing (ISO 8601 format)", alias="ToTime") target_dataset_id: Optional[StrictStr] = Field(default=None, description="Target dataset ID for BDA extraction output", alias="TargetDatasetId") target_dataset_provided: Optional[StrictBool] = Field(default=None, description="Flag indicating if target dataset is provided", alias="TargetDatasetProvided") __properties: ClassVar[List[str]] = ["NumberOfWorkers", "Timeout", "FileProcessingMode", "FileIds", "FromTime", "ToTime", "TargetDatasetId", "TargetDatasetProvided"]
[docs] @field_validator('file_processing_mode') def file_processing_mode_validate_enum(cls, value): """Validates the enum""" if value is None: return value if value not in set(['all', 'time-based', 'cdc', 'file']): raise ValueError("must be one of enum values ('all', 'time-based', 'cdc', 'file')") return value
model_config = ConfigDict( populate_by_name=True, validate_assignment=True, protected_namespaces=(), )
[docs] def to_str(self) -> str: """Returns the string representation of the model using alias""" return pprint.pformat(self.model_dump(by_alias=True))
[docs] def to_json(self) -> str: """Returns the JSON representation of the model using alias""" # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead return json.dumps(self.to_dict())
[docs] @classmethod def from_json(cls, json_str: str) -> Optional[Self]: """Create an instance of AdhocTriggerJobRequest from a JSON string""" return cls.from_dict(json.loads(json_str))
[docs] 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
[docs] @classmethod def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]: """Create an instance of AdhocTriggerJobRequest from a dict""" if obj is None: return None if not isinstance(obj, dict): return cls.model_validate(obj) _obj = cls.model_validate({ "NumberOfWorkers": obj.get("NumberOfWorkers"), "Timeout": obj.get("Timeout"), "FileProcessingMode": obj.get("FileProcessingMode"), "FileIds": obj.get("FileIds"), "FromTime": obj.get("FromTime"), "ToTime": obj.get("ToTime"), "TargetDatasetId": obj.get("TargetDatasetId"), "TargetDatasetProvided": obj.get("TargetDatasetProvided") }) return _obj