"""Service object implementation for SQLAlchemy.
RepositoryService object is generic on the domain model type which
should be a SQLAlchemy model.
"""
from __future__ import annotations
from functools import partial
from pathlib import Path, PurePath
from typing import (
TYPE_CHECKING,
Any,
Callable,
List,
Sequence,
cast,
overload,
)
from uuid import UUID
from advanced_alchemy.exceptions import AdvancedAlchemyError
from advanced_alchemy.filters import LimitOffset
from advanced_alchemy.repository.typing import ModelOrRowMappingT
from advanced_alchemy.service.pagination import OffsetPagination
from advanced_alchemy.service.typing import (
MSGSPEC_INSTALLED,
PYDANTIC_INSTALLED,
BaseModel,
ModelDTOT,
Struct,
convert,
get_type_adapter,
)
if TYPE_CHECKING:
from sqlalchemy import ColumnElement, RowMapping
from advanced_alchemy.base import ModelProtocol
from advanced_alchemy.filters import StatementFilter
from advanced_alchemy.service.typing import FilterTypeT
def _default_msgspec_deserializer(
target_type: Any,
value: Any,
type_decoders: Sequence[tuple[Callable[[Any], bool], Callable[[Any, Any], Any]]] | None = None,
) -> Any: # pragma: no cover
"""Transform values non-natively supported by ``msgspec``
Args:
target_type: Encountered type
value: Value to coerce
type_decoders: Optional sequence of type decoders
Returns:
A ``msgspec``-supported type
"""
if isinstance(value, target_type):
return value
if type_decoders:
for predicate, decoder in type_decoders:
if predicate(target_type):
return decoder(target_type, value)
if issubclass(target_type, (Path, PurePath, UUID)):
return target_type(value)
msg = f"Unsupported type: {type(value)!r}"
raise TypeError(msg)
[docs]def find_filter(
filter_type: type[FilterTypeT],
filters: Sequence[StatementFilter | ColumnElement[bool]] | Sequence[StatementFilter],
) -> FilterTypeT | None:
"""Get the filter specified by filter type from the filters.
Args:
filter_type: The type of filter to find.
filters: filter types to apply to the query
Returns:
The match filter instance or None
"""
return next(
(cast("FilterTypeT | None", filter_) for filter_ in filters if isinstance(filter_, filter_type)),
None,
)
[docs]class ResultConverter:
"""Simple mixin to help convert to a paginated response model the results set is a list."""
@overload
def to_schema(
self,
data: ModelOrRowMappingT,
total: int | None = None,
filters: Sequence[StatementFilter | ColumnElement[bool]] | Sequence[StatementFilter] | None = None,
*,
schema_type: None = None,
) -> ModelOrRowMappingT: ...
@overload
def to_schema(
self,
data: Sequence[ModelOrRowMappingT],
total: int | None = None,
filters: Sequence[StatementFilter | ColumnElement[bool]] | Sequence[StatementFilter] | None = None,
*,
schema_type: None = None,
) -> OffsetPagination[ModelOrRowMappingT]: ...
@overload
def to_schema(
self,
data: ModelProtocol | RowMapping,
total: int | None = None,
filters: Sequence[StatementFilter | ColumnElement[bool]] | Sequence[StatementFilter] | None = None,
*,
schema_type: type[ModelDTOT],
) -> ModelDTOT: ...
@overload
def to_schema(
self,
data: Sequence[ModelProtocol] | Sequence[RowMapping],
total: int | None = None,
filters: Sequence[StatementFilter | ColumnElement[bool]] | Sequence[StatementFilter] | None = None,
*,
schema_type: type[ModelDTOT],
) -> OffsetPagination[ModelDTOT]: ...
[docs] def to_schema(
self,
data: ModelOrRowMappingT
| Sequence[ModelOrRowMappingT]
| ModelProtocol
| Sequence[ModelProtocol]
| RowMapping
| Sequence[RowMapping],
total: int | None = None,
filters: Sequence[StatementFilter | ColumnElement[bool]] | Sequence[StatementFilter] | None = None,
*,
schema_type: type[ModelDTOT] | None = None,
) -> ModelOrRowMappingT | OffsetPagination[ModelOrRowMappingT] | ModelDTOT | OffsetPagination[ModelDTOT]:
"""Convert the object to a response schema. When `schema_type` is None, the model is returned with no conversion.
Args:
data: The return from one of the service calls.
total: the total number of rows in the data
filters: Collection route filters.
schema_type: Collection route filters.
Returns:
The list of instances retrieved from the repository.
"""
if filters is None:
filters = []
if schema_type is None:
if not isinstance(data, Sequence):
return cast("ModelOrRowMappingT", data)
limit_offset = find_filter(LimitOffset, filters=filters)
total = total or len(data)
limit_offset = limit_offset if limit_offset is not None else LimitOffset(limit=len(data), offset=0)
return OffsetPagination[ModelOrRowMappingT](
items=cast("Sequence[ModelOrRowMappingT]", data),
limit=limit_offset.limit,
offset=limit_offset.offset,
total=total,
)
if MSGSPEC_INSTALLED and issubclass(schema_type, Struct):
if not isinstance(data, Sequence):
return cast(
"ModelDTOT",
convert(
obj=data,
type=schema_type,
from_attributes=True,
dec_hook=partial(
_default_msgspec_deserializer,
type_decoders=[
(lambda x: x is UUID, lambda t, v: t(v.hex)),
],
),
),
)
limit_offset = find_filter(LimitOffset, filters=filters)
total = total or len(data)
limit_offset = limit_offset if limit_offset is not None else LimitOffset(limit=len(data), offset=0)
return OffsetPagination[ModelDTOT](
items=convert(
obj=data,
type=List[schema_type], # type: ignore[valid-type]
from_attributes=True,
dec_hook=partial(
_default_msgspec_deserializer,
type_decoders=[
(lambda x: x is UUID, lambda t, v: t(v.hex)),
],
),
),
limit=limit_offset.limit,
offset=limit_offset.offset,
total=total,
)
if PYDANTIC_INSTALLED and issubclass(schema_type, BaseModel):
if not isinstance(data, Sequence):
return cast(
"ModelDTOT",
get_type_adapter(schema_type).validate_python(data, from_attributes=True),
) # pyright: ignore[reportUnknownVariableType,reportUnknownMemberType,reportAttributeAccessIssue,reportCallIssue]
limit_offset = find_filter(LimitOffset, filters=filters)
total = total if total else len(data)
limit_offset = limit_offset if limit_offset is not None else LimitOffset(limit=len(data), offset=0)
return OffsetPagination[ModelDTOT](
items=get_type_adapter(List[schema_type]).validate_python(data, from_attributes=True), # type: ignore[valid-type] # pyright: ignore[reportUnknownArgumentType]
limit=limit_offset.limit,
offset=limit_offset.offset,
total=total,
)
msg = "`schema_type` should be a valid Pydantic or Msgspec schema"
raise AdvancedAlchemyError(msg)
advanced_alchemy