# 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.component_basic_details import ComponentBasicDetails
from typing import Optional, Set
from typing_extensions import Self
[docs]
class AIModelDetails(BaseModel):
"""
AIModelDetails
""" # noqa: E501
model_id: Optional[StrictStr] = Field(default=None, alias="ModelId")
model_name: Optional[StrictStr] = Field(default=None, alias="ModelName")
model_version: Optional[StrictStr] = Field(default=None, alias="ModelVersion")
model_token_limit: Optional[StrictStr] = Field(default=None, alias="ModelTokenLimit")
model_arn: Optional[StrictStr] = Field(default=None, alias="ModelArn")
model_status: Optional[StrictStr] = Field(default=None, alias="ModelStatus")
provider_name: Optional[StrictStr] = Field(default=None, alias="ProviderName")
model_status_message: Optional[StrictStr] = Field(default=None, alias="ModelStatusMessage")
model_type: Optional[StrictStr] = Field(default=None, alias="ModelType")
model_description: Optional[StrictStr] = Field(default=None, alias="ModelDescription")
model_parameters: Optional[Dict[str, Any]] = Field(default=None, alias="ModelParameters")
model_traits: Optional[StrictStr] = Field(default=None, alias="ModelTraits")
input_modalities: Optional[List[StrictStr]] = Field(default=None, alias="InputModalities")
output_modalities: Optional[List[StrictStr]] = Field(default=None, alias="OutputModalities")
inference_types_supported: Optional[List[StrictStr]] = Field(default=None, alias="InferenceTypesSupported")
last_modified_time: Optional[StrictStr] = Field(default=None, alias="LastModifiedTime")
last_modified_by: Optional[StrictStr] = Field(default=None, alias="LastModifiedBy")
default_inference_type: Optional[StrictStr] = Field(default=None, alias="DefaultInferenceType")
inference_profile_details: Optional[Dict[str, Any]] = Field(default=None, alias="InferenceProfileDetails")
components: Optional[List[ComponentBasicDetails]] = Field(default=None, alias="Components")
__properties: ClassVar[List[str]] = ["ModelId", "ModelName", "ModelVersion", "ModelTokenLimit", "ModelArn", "ModelStatus", "ProviderName", "ModelStatusMessage", "ModelType", "ModelDescription", "ModelParameters", "ModelTraits", "InputModalities", "OutputModalities", "InferenceTypesSupported", "LastModifiedTime", "LastModifiedBy", "DefaultInferenceType", "InferenceProfileDetails", "Components"]
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 AIModelDetails 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,
)
# override the default output from pydantic by calling `to_dict()` of each item in components (list)
_items = []
if self.components:
for _item_components in self.components:
if _item_components:
_items.append(_item_components.to_dict())
_dict['Components'] = _items
return _dict
[docs]
@classmethod
def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
"""Create an instance of AIModelDetails from a dict"""
if obj is None:
return None
if not isinstance(obj, dict):
return cls.model_validate(obj)
_obj = cls.model_validate({
"ModelId": obj.get("ModelId"),
"ModelName": obj.get("ModelName"),
"ModelVersion": obj.get("ModelVersion"),
"ModelTokenLimit": obj.get("ModelTokenLimit"),
"ModelArn": obj.get("ModelArn"),
"ModelStatus": obj.get("ModelStatus"),
"ProviderName": obj.get("ProviderName"),
"ModelStatusMessage": obj.get("ModelStatusMessage"),
"ModelType": obj.get("ModelType"),
"ModelDescription": obj.get("ModelDescription"),
"ModelParameters": obj.get("ModelParameters"),
"ModelTraits": obj.get("ModelTraits"),
"InputModalities": obj.get("InputModalities"),
"OutputModalities": obj.get("OutputModalities"),
"InferenceTypesSupported": obj.get("InferenceTypesSupported"),
"LastModifiedTime": obj.get("LastModifiedTime"),
"LastModifiedBy": obj.get("LastModifiedBy"),
"DefaultInferenceType": obj.get("DefaultInferenceType"),
"InferenceProfileDetails": obj.get("InferenceProfileDetails"),
"Components": [ComponentBasicDetails.from_dict(_item) for _item in obj["Components"]] if obj.get("Components") is not None else None
})
return _obj