def make_output_factory_args_kwargs(self, mapping_items):
# type: (NamespaceMappingOrIterable) -> Tuple[Iterable, Mapping[Name, Value]]
try:
if isinstance(mapping_items, (str, bytes, bytearray)):
raise DataConversionError('got a `str`/`bytes`/`bytearray`')
self.verify_isinstance(mapping_items, (Mapping, Iterable))
mapping = dict(**dict(mapping_items))
except (TypeError, DataConversionError):
raise DataConversionError(
'expected a name-to-value mapping or an equivalent '
'collection of name-value pairs')
kwargs = dict(self.output_factory_default_kwargs)
kwargs.update(mapping)
return (), kwargs
def _adjust_server_secret(value):
# type: (...) -> str
if not isinstance(value, (unicode, bytes)): #3: `unicode` -> `str`
raise DataConversionError('not a `str` or `bytes` - its type is `{}`'
.format(ascii_str(type(value).__name__))) #3: `__name__` -> `__qualname__`
try:
value = as_unicode(value)
except UnicodeDecodeError:
# We don't want to reveal the value in the traceback etc.
raise DataConversionError('contains non-UTF-8 binary data') #3: add: `from None`
if not value.strip():
raise DataConversionError('an empty or whitespace-only value')
value = value.encode('utf-8') #3--
assert isinstance(value, str)
yield value
def _verify_length(self, value):
value_length = len(value)
if self.max_length is not None and value_length > self.max_length:
raise DataConversionError(
'too long string (its length: {} characters; '
'maximum valid length: {} characters)'.format(
value_length, self.max_length))
def generate_output_items(self):
# type: () -> Iterator[NameValuePair]
for (output_name, converted_value_cell
) in self._output_name_to_converted_value_cell.items():
possible_input_names = self.iter_input_names_for_output_name(
output_name)
with DataConversionError.sublocation(possible_input_names):
yield output_name, converted_value_cell.output
def _adjust_token_max_age(value):
# type: (...) -> int
orig_value = value
value = int(value)
if value == orig_value and not isinstance(orig_value, bool):
yield value
else:
raise DataConversionError('{!r} is not an integer number'.format(orig_value))
def _convert_flag_string(self, value):
word = value.lower()
if not self._true_flag_strings and not self._false_flag_strings:
raise DataConversionError(
'{!a} is an illegal value (for this item, strings '
'cannot be converted to Boolean values)'.format(word))
if word in self._true_flag_strings:
word = True
elif word in self._false_flag_strings:
word = False
else:
raise DataConversionError(
'{!a} is an illegal value (for this item these are the only '
'strings that can be converted to Boolean values: {})'.format(
word, ', '.join(
sorted(map(ascii, self._true_flag_strings)) +
sorted(map(ascii, self._false_flag_strings)))))
return word
def verify_required_input_items_collected(self, required_input_names):
# type: (AbstractSet) -> None
uncollected = required_input_names - self._collected_input_names
if uncollected:
raise DataConversionError(
'the following required items are missing or have '
'been skipped as effectively NULL-like (i.e., not '
'carrying any meaningful data): {}'.format(', '.join(
sorted(
repr(ascii_str(input_name))
for input_name in uncollected))))
def verify_is_regular_iterable_collection(cls, value):
# type: (Value) -> None
"""
A helper that can to be used in subclasses: verify that for the
given `value` --
* `isinstance(value, typing.Iterable)` is true, **but**
* `isinstance(value, typing.Mapping)` is *not* true, and
* `isinstance(value, str)` is *not* true, and
* `isinstance(value, (bytes, bytearray, memoryview))` is *not* true.
If the result of the above verification is negative, a
`DataConversionError` with an appropriate message is raised.
"""
cls.verify_isinstance(value, Iterable, 'expected a collection')
if isinstance(value, Mapping):
raise DataConversionError(
'expected a collection but *not* a mapping')
if isinstance(value, str):
raise DataConversionError('expected a collection, *not* a string')
if isinstance(value, (bytes, bytearray, memoryview)):
raise DataConversionError(
'expected a collection, *not* a binary data object')
def verify_in(value, container, error_message=None):
# type: (Value, Container, Optional[String]) -> None
"""
A helper that can to be used in subclasses: verify
that `value in container` is true. If not, raise
`DataConversionError(error_message)` or -- if
`error_message` is unspecified or specified as
`None` -- a `DataConversionError` with an automatically
generated `"... is not in ..."` message.
"""
if value not in container:
if error_message is None:
error_message = '{!a} is not in {!a}'.format(value, container)
raise DataConversionError(error_message)
def validate_input_names(self, data):
# type: (Iterator) -> (Sequence[NameValuePair])
"""
Note: this method works in an eager (not lazy) manner (in
particular, is not a generator and does not return an iterator,
but a sequence) -- because we want to emphasize that we do
*not* want to defer the validation, but to have it performed
immediately.
"""
validated = []
for input_name, input_value in data:
if not isinstance(input_name, str):
raise DataConversionError(
'unexpected non-string key ({!a}) in '
'the mapping'.format(input_name))
validated.append((input_name, input_value))
return validated
def verify_isinstance(value, type_spec, error_message=None):
# type: (Value, TypeSpec, Optional[String]) -> None
"""
A helper that can to be used in subclasses: verify that
`isinstance(value, type_spec)` is true. If not, raise
`DataConversionError(error_message)` or -- if `error_message`
is unspecified or specified as `None` -- a `DataConversionError`
with an automatically generated `"unexpected type of..."`-like
message.
"""
if not isinstance(value, type_spec):
if error_message is None:
if isinstance(type_spec, tuple):
error_message = 'unexpected type of {!a}'.format(value)
else:
safe_class_name = ascii_str(type_spec.__qualname__)
error_message = 'unexpected type of {!a} (should be a {})'.format(
value, safe_class_name)
raise DataConversionError(error_message)
def __call__(self, data):
# type: (Value) -> Iterator
with self.state_bookkeeper_context() as bookkeeper:
input_name_value_pairs = self.generate_input_name_value_pairs(data)
input_name_value_pairs = self.validate_input_names(
input_name_value_pairs)
input_name_value_pairs = bookkeeper.preprocess_input_items(
input_name_value_pairs)
for input_name, input_value in input_name_value_pairs:
with DataConversionError.sublocation(input_name):
item_conv = self._input_name_to_converter.get(
input_name, self._free_item_converter)
for output_name, converted_value_cell in item_conv(
(input_name, input_value)):
bookkeeper.collect_converted_item(
input_name, output_name, converted_value_cell)
bookkeeper.verify_required_input_items_collected(
self._required_input_names)
output_name_value_pairs = bookkeeper.generate_output_items()
for assembled_data in self._assembling_converter(
output_name_value_pairs):
yield assembled_data
def __add__(self, other):
# type: (Cell) -> Cell
if isinstance(other, StemCell):
return self
raise DataConversionError('data duplication detected')
def illegal_item_error_factory(_data):
return DataConversionError('illegal item name')
def generate_output_elements(self, input_elements):
# type: (Iterator[Value]) -> Iterator[Value]
for index, value in enumerate(input_elements):
with DataConversionError.sublocation(index):
for converted_value in self._element_converter(value):
yield converted_value
def default_error_factory(_data):
# type: (Value) -> BaseException
return DataConversionError('unspecified error')
