Source code for openapi_client.models.metadata_generator_output_payload

# coding: utf-8

"""
    Amorphic Data Platform

    Amorphic Data Platform - API Definition documentation

    The version of the OpenAPI document: 0.3.0
    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, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from openapi_client.models.dataset_schema import DatasetSchema
from typing import Optional, Set
from typing_extensions import Self



class MetadataGeneratorOutputPayload(BaseModel):
    """
    MetadataGeneratorOutputPayload
    """ # noqa: E501
    dataset_name: Optional[StrictStr] = Field(default=None, alias="DatasetName")
    dataset_description: Optional[StrictStr] = Field(default=None, alias="DatasetDescription")
    domain: Optional[StrictStr] = Field(default=None, alias="Domain")
    keywords: Optional[List[StrictStr]] = Field(default=None, alias="Keywords")
    dataset_schema: Optional[List[DatasetSchema]] = Field(default=None, alias="DatasetSchema")
    target_location: Optional[StrictStr] = Field(default=None, alias="TargetLocation")
    datasource_type: Optional[StrictStr] = Field(default=None, alias="DatasourceType")
    file_type: Optional[StrictStr] = Field(default=None, alias="FileType")
    table_update: Optional[StrictStr] = Field(default=None, alias="TableUpdate")
    dataset_type: Optional[StrictStr] = Field(default=None, alias="DatasetType")
    __properties: ClassVar[List[str]] = ["DatasetName", "DatasetDescription", "Domain", "Keywords", "DatasetSchema", "TargetLocation", "DatasourceType", "FileType", "TableUpdate", "DatasetType"]

    model_config = ConfigDict(
        populate_by_name=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"""
        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
        return json.dumps(self.to_dict())




    @classmethod
    def from_json(cls, json_str: str) -> Optional[Self]:
        """Create an instance of MetadataGeneratorOutputPayload 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 dataset_schema (list)
        _items = []
        if self.dataset_schema:
            for _item_dataset_schema in self.dataset_schema:
                if _item_dataset_schema:
                    _items.append(_item_dataset_schema.to_dict())
            _dict['DatasetSchema'] = _items
        return _dict




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

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

        _obj = cls.model_validate({
            "DatasetName": obj.get("DatasetName"),
            "DatasetDescription": obj.get("DatasetDescription"),
            "Domain": obj.get("Domain"),
            "Keywords": obj.get("Keywords"),
            "DatasetSchema": [DatasetSchema.from_dict(_item) for _item in obj["DatasetSchema"]] if obj.get("DatasetSchema") is not None else None,
            "TargetLocation": obj.get("TargetLocation"),
            "DatasourceType": obj.get("DatasourceType"),
            "FileType": obj.get("FileType"),
            "TableUpdate": obj.get("TableUpdate"),
            "DatasetType": obj.get("DatasetType")
        })
        return _obj