def test_subset_dataclass_optional_subset():
Foo = make_datacls("Foo", (("a", int), ("b", str), ("c", float)))
optional = {"a", "b"}
Bar = derive_datacls("Bar", Foo, optional=optional)
for f in fields(Bar):
assert (f.name in optional
and is_optional(f.type)) or not is_optional(f.type)
def test_is_optional():
assert is_optional(Optional[str])
assert is_optional(Annotated[Optional[str], ""])
assert is_optional(Annotated[Union[str, Annotated[Optional[int], ""]], ""])
assert not is_optional(str)
assert not is_optional(Annotated[str, ""])
assert not is_optional(Annotated[Union[str, Annotated[int, ""]], ""])
def decode(self, value: Any) -> python_type:
if not isinstance(value, dict):
raise DecodeError
kwargs = {}
for field in fields:
codec = get_codec(JSON, field.type)
try:
with CodecError.path_on_error(field.name):
kwargs[field.name] = codec.decode(value[_dc_kw.get(
field.name, field.name)])
except KeyError:
if (is_optional(field.type)
and field.default is dataclasses.MISSING and
field.default_factory is dataclasses.MISSING):
kwargs[field.name] = None
try:
return python_type(**kwargs)
except Exception as e:
raise DecodeError from e
def __init__(self, **kwargs):
hints = get_type_hints(self, include_extras=True)
for name in kwargs:
if name not in fields:
raise TypeError(f"unexpected keyword argument: '{name}'")
for field in fields.values():
if (value := kwargs.get(field.name, MISSING)) is MISSING:
if field.default_factory is not dataclasses.MISSING:
value = field.default_factory()
elif field.default is not dataclasses.MISSING:
value = field.default
elif is_optional(hints[field.name]):
value = None
else:
raise TypeError(
f"missing required keyword argument: '{field.name}'")
setattr(self, field.name, value)
def _dataclass_schema(*, python_type, annotated, origin, args, processor, **_):
if dataclasses.is_dataclass(python_type):
if ref := processor.references.get(python_type):
return ref
component_schema = _get_component_schema(annotated)
if component_schema:
name = component_schema.name or processor.component_schema_name(
python_type.__name__
)
ref = {"$ref": f"#/components/schemas/{name}"}
processor.references[python_type] = ref
hints = typing.get_type_hints(python_type, include_extras=True)
required = {
f.name
for f in dataclasses.fields(python_type)
if f.default is dataclasses.MISSING
and f.default_factory is dataclasses.MISSING
and not is_optional(hints[f.name])
}
properties = {
_dc_kw.get(key, key): processor.schema(pytype) for key, pytype in hints.items()
}
for key, schema in properties.items():
if key not in required and not fondat.validation.is_valid(schema, Reference):
schema.nullable = None
schema = Schema(
type="object",
properties=properties,
required=required or None,
additionalProperties=False,
**_kwargs(python_type, annotated),
)
if component_schema:
processor.openapi.components.schemas[name] = schema
return ref
return schema
async def _handle(self, request: Request) -> Response:
if not request.path.startswith(self.path):
raise NotFoundError
path = request.path[len(self.path):]
response = Response()
method = request.method.lower()
segments = path.split("/") if path else ()
resource = self.root
operation = None
for segment in segments:
if operation: # cannot have segments after operation name
raise NotFoundError
try:
resource = await _subordinate(resource, segment)
except NotFoundError:
try:
operation = getattr(resource, segment)
if not fondat.resource.is_operation(operation):
raise NotFoundError
except AttributeError:
raise NotFoundError
if operation: # operation name as segment (@query or @mutation)
fondat_op = getattr(operation, "_fondat_operation", None)
if not fondat_op or not fondat_op.method == method:
raise MethodNotAllowedError
else: # no remaining segments; operation name as HTTP method
operation = getattr(resource, method, None)
if not fondat.resource.is_operation(operation):
raise MethodNotAllowedError
body = await _decode_body(operation, request)
params = {}
signature = inspect.signature(operation)
hints = typing.get_type_hints(operation, include_extras=True)
return_hint = hints.get("return", type(None))
for name, hint in hints.items():
if name == "return":
continue
required = signature.parameters[
name].default is inspect.Parameter.empty
param_in = get_param_in(operation, name, hint)
if isinstance(param_in, AsBody) and body is not None:
params[name] = body
elif isinstance(param_in, InBody) and body is not None:
if param_in.name in body:
params[name] = body[param_in.name]
elif isinstance(param_in, InQuery):
if param_in.name in request.query:
codec = get_codec(String, hint)
try:
with DecodeError.path_on_error(param_in.name):
params[name] = codec.decode(
request.query[param_in.name])
except DecodeError as de:
raise BadRequestError from de
if name not in params and required:
if not is_optional(hint):
raise BadRequestError from DecodeError(
"required parameter", ["«params»", name])
params[name] = None
result = await operation(**params)
if not is_subclass(return_hint, Stream):
return_codec = get_codec(Binary, return_hint)
try:
result = BytesStream(return_codec.encode(result),
return_codec.content_type)
except Exception as e:
raise InternalServerError from e
response.body = result
response.headers["Content-Type"] = response.body.content_type
if response.body.content_length is not None:
if response.body.content_length == 0:
response.status = http.HTTPStatus.NO_CONTENT.value
else:
response.headers["Content-Length"] = str(
response.body.content_length)
return response
def test_derive_datacls_optional_true():
Foo = make_datacls("Foo", (("a", int), ("b", str), ("c", float)))
Bar = derive_datacls("Bar", Foo, optional=True)
for field in fields(Bar):
assert is_optional(field.type)
def typeddict_codec(
typeddict: Any,
columns: Optional[Sequence[str]] = None,
keys: Optional[Mapping[str, str]] = None,
codecs: Optional[Mapping[str, Any]] = None,
):
"""
Return a codec that encodes/decodes a typed dictionary to/from a CSV row. A CSV row is
represented as a list of strings.
Parameters:
• typeddict: TypedDict type to encode/decode
• columns: sequence of column names
• keys: mapping between columns and dictionary keys
• codecs: mapping between columns and codecs
The columns parameter specifies the names of CSV columns, and the order they are encoded
in a row. If the columns parameter is omitted, then columns will be all dictionary keys,
in the order they are defined in the TypedDict.
The keys mapping specifies the mapping between columns and dictionary keys. If no mapping
for a given column is specified, then the column will map the to dictionary key of the
same name.
The codecs mapping specifies which codecs are used to encode columns. If no mapping for a
given column is provided, then the default codec for its associated field is used.
"""
if not is_subclass(typeddict, dict) or getattr(
typeddict, "__annotations__", None) is None:
raise TypeError("typeddict parameter must be a TypedDict")
hints = get_type_hints(typeddict, include_extras=True)
if columns is None:
columns = tuple(key for key in hints.keys())
if keys is None:
keys = {key: key for key in hints}
keys = {column: key for column, key in keys.items() if column in columns}
if codecs is None:
codecs = {}
codecs = {
column: codecs.get(column, get_codec(String, hints[keys[column]]))
for column in columns if column in keys
}
optional_fields = {key for key in keys if is_optional(hints[key])}
class TypedDictRowCodec(Codec[typeddict, list[str]]):
"""Encodes/decodes a dataclass to/from a CSV row."""
def __init__(self, columns: Sequence[str]):
self.columns = columns
def encode(self, value: typeddict) -> list[str]:
"""
Encode from TypedDict value to CSV row. If a field value is None, it will be
represented in a column as an empty string.
"""
return [
codecs[column].encode(value.get(keys[column]))
for column in self.columns
]
def decode(self, values: list[str]) -> typeddict:
"""
Decode from CSV row to TypedDict value. If a column to decode contains an empty
string value, it will be represented as None if the associated field is optional.
"""
items = {}
for column, value in zip(self.columns, values):
key = keys.get(column)
if not key: # ignore unmapped column
continue
if value == "" and key in optional_fields:
items[key] = None
else:
with DecodeError.path_on_error(column):
items[key] = codecs[column].decode(value)
return typeddict(items)
return TypedDictRowCodec(columns=columns)
