Home-IoT/craftbeerpi4-pione
1
0
Fork 0
mirror of https://github.com/PiBrewing/craftbeerpi4.git synced 2024-11-10 01:17:42 +01:00
Code Issues Projects Releases Packages Wiki Activity
craftbeerpi4-pione/venv3/lib/python3.7/site-packages/voluptuous/schema_builder.py

1302 lines
43 KiB
Python
Raw Normal View History

"fix cli main_dir error"
2021-03-03 23:49:41 +01:00
import collections
import inspect
import re
from functools import wraps
import sys
from contextlib import contextmanager
import itertools
from voluptuous import error as er
if sys.version_info >= (3,):
long = int
unicode = str
basestring = str
ifilter = filter
def iteritems(d):
return d.items()
else:
from itertools import ifilter
def iteritems(d):
return d.iteritems()
if sys.version_info >= (3, 3):
_Mapping = collections.abc.Mapping
else:
_Mapping = collections.Mapping
"""Schema validation for Python data structures.
Given eg. a nested data structure like this:
{
'exclude': ['Users', 'Uptime'],
'include': [],
'set': {
'snmp_community': 'public',
'snmp_timeout': 15,
'snmp_version': '2c',
},
'targets': {
'localhost': {
'exclude': ['Uptime'],
'features': {
'Uptime': {
'retries': 3,
},
'Users': {
'snmp_community': 'monkey',
'snmp_port': 15,
},
},
'include': ['Users'],
'set': {
'snmp_community': 'monkeys',
},
},
},
}
A schema like this:
>>> settings = {
... 'snmp_community': str,
... 'retries': int,
... 'snmp_version': All(Coerce(str), Any('3', '2c', '1')),
... }
>>> features = ['Ping', 'Uptime', 'Http']
>>> schema = Schema({
... 'exclude': features,
... 'include': features,
... 'set': settings,
... 'targets': {
... 'exclude': features,
... 'include': features,
... 'features': {
... str: settings,
... },
... },
... })
Validate like so:
>>> schema({
... 'set': {
... 'snmp_community': 'public',
... 'snmp_version': '2c',
... },
... 'targets': {
... 'exclude': ['Ping'],
... 'features': {
... 'Uptime': {'retries': 3},
... 'Users': {'snmp_community': 'monkey'},
... },
... },
... }) == {
... 'set': {'snmp_version': '2c', 'snmp_community': 'public'},
... 'targets': {
... 'exclude': ['Ping'],
... 'features': {'Uptime': {'retries': 3},
... 'Users': {'snmp_community': 'monkey'}}}}
True
"""
# options for extra keys
PREVENT_EXTRA = 0 # any extra key not in schema will raise an error
ALLOW_EXTRA = 1 # extra keys not in schema will be included in output
REMOVE_EXTRA = 2 # extra keys not in schema will be excluded from output
def _isnamedtuple(obj):
return isinstance(obj, tuple) and hasattr(obj, '_fields')
primitive_types = (str, unicode, bool, int, float)
class Undefined(object):
def __nonzero__(self):
return False
def __repr__(self):
return '...'
UNDEFINED = Undefined()
def Self():
raise er.SchemaError('"Self" should never be called')
def default_factory(value):
if value is UNDEFINED or callable(value):
return value
return lambda: value
@contextmanager
def raises(exc, msg=None, regex=None):
try:
yield
except exc as e:
if msg is not None:
assert str(e) == msg, '%r != %r' % (str(e), msg)
if regex is not None:
assert re.search(regex, str(e)), '%r does not match %r' % (str(e), regex)
def Extra(_):
"""Allow keys in the data that are not present in the schema."""
raise er.SchemaError('"Extra" should never be called')
# As extra() is never called there's no way to catch references to the
# deprecated object, so we just leave an alias here instead.
extra = Extra
class Schema(object):
"""A validation schema.
The schema is a Python tree-like structure where nodes are pattern
matched against corresponding trees of values.
Nodes can be values, in which case a direct comparison is used, types,
in which case an isinstance() check is performed, or callables, which will
validate and optionally convert the value.
We can equate schemas also.
For Example:
>>> v = Schema({Required('a'): unicode})
>>> v1 = Schema({Required('a'): unicode})
>>> v2 = Schema({Required('b'): unicode})
>>> assert v == v1
>>> assert v != v2
"""
_extra_to_name = {
REMOVE_EXTRA: 'REMOVE_EXTRA',
ALLOW_EXTRA: 'ALLOW_EXTRA',
PREVENT_EXTRA: 'PREVENT_EXTRA',
}
def __init__(self, schema, required=False, extra=PREVENT_EXTRA):
"""Create a new Schema.
:param schema: Validation schema. See :module:`voluptuous` for details.
:param required: Keys defined in the schema must be in the data.
:param extra: Specify how extra keys in the data are treated:
- :const:`~voluptuous.PREVENT_EXTRA`: to disallow any undefined
extra keys (raise ``Invalid``).
- :const:`~voluptuous.ALLOW_EXTRA`: to include undefined extra
keys in the output.
- :const:`~voluptuous.REMOVE_EXTRA`: to exclude undefined extra keys
from the output.
- Any value other than the above defaults to
:const:`~voluptuous.PREVENT_EXTRA`
"""
self.schema = schema
self.required = required
self.extra = int(extra) # ensure the value is an integer
self._compiled = self._compile(schema)
@classmethod
def infer(cls, data, **kwargs):
"""Create a Schema from concrete data (e.g. an API response).
For example, this will take a dict like:
{
'foo': 1,
'bar': {
'a': True,
'b': False
},
'baz': ['purple', 'monkey', 'dishwasher']
}
And return a Schema:
{
'foo': int,
'bar': {
'a': bool,
'b': bool
},
'baz': [str]
}
Note: only very basic inference is supported.
"""
def value_to_schema_type(value):
if isinstance(value, dict):
if len(value) == 0:
return dict
return {k: value_to_schema_type(v)
for k, v in iteritems(value)}
if isinstance(value, list):
if len(value) == 0:
return list
else:
return [value_to_schema_type(v)
for v in value]
return type(value)
return cls(value_to_schema_type(data), **kwargs)
def __eq__(self, other):
if not isinstance(other, Schema):
return False
return other.schema == self.schema
def __ne__(self, other):
return not (self == other)
def __str__(self):
return str(self.schema)
def __repr__(self):
return "<Schema(%s, extra=%s, required=%s) object at 0x%x>" % (
self.schema, self._extra_to_name.get(self.extra, '??'),
self.required, id(self))
def __call__(self, data):
"""Validate data against this schema."""
try:
return self._compiled([], data)
except er.MultipleInvalid:
raise
except er.Invalid as e:
raise er.MultipleInvalid([e])
# return self.validate([], self.schema, data)
def _compile(self, schema):
if schema is Extra:
return lambda _, v: v
if schema is Self:
return lambda p, v: self._compiled(p, v)
elif hasattr(schema, "__voluptuous_compile__"):
return schema.__voluptuous_compile__(self)
if isinstance(schema, Object):
return self._compile_object(schema)
if isinstance(schema, _Mapping):
return self._compile_dict(schema)
elif isinstance(schema, list):
return self._compile_list(schema)
elif isinstance(schema, tuple):
return self._compile_tuple(schema)
elif isinstance(schema, (frozenset, set)):
return self._compile_set(schema)
type_ = type(schema)
if inspect.isclass(schema):
type_ = schema
if type_ in (bool, bytes, int, long, str, unicode, float, complex, object,
list, dict, type(None)) or callable(schema):
return _compile_scalar(schema)
raise er.SchemaError('unsupported schema data type %r' %
type(schema).__name__)
def _compile_mapping(self, schema, invalid_msg=None):
"""Create validator for given mapping."""
invalid_msg = invalid_msg or 'mapping value'
# Keys that may be required
all_required_keys = set(key for key in schema
if key is not Extra and
((self.required and not isinstance(key, (Optional, Remove))) or
isinstance(key, Required)))
# Keys that may have defaults
all_default_keys = set(key for key in schema
if isinstance(key, Required) or
isinstance(key, Optional))
_compiled_schema = {}
for skey, svalue in iteritems(schema):
new_key = self._compile(skey)
new_value = self._compile(svalue)
_compiled_schema[skey] = (new_key, new_value)
candidates = list(_iterate_mapping_candidates(_compiled_schema))
# After we have the list of candidates in the correct order, we want to apply some optimization so that each
# key in the data being validated will be matched against the relevant schema keys only.
# No point in matching against different keys
additional_candidates = []
candidates_by_key = {}
for skey, (ckey, cvalue) in candidates:
if type(skey) in primitive_types:
candidates_by_key.setdefault(skey, []).append((skey, (ckey, cvalue)))
elif isinstance(skey, Marker) and type(skey.schema) in primitive_types:
candidates_by_key.setdefault(skey.schema, []).append((skey, (ckey, cvalue)))
else:
# These are wildcards such as 'int', 'str', 'Remove' and others which should be applied to all keys
additional_candidates.append((skey, (ckey, cvalue)))
def validate_mapping(path, iterable, out):
required_keys = all_required_keys.copy()
# Build a map of all provided key-value pairs.
# The type(out) is used to retain ordering in case a ordered
# map type is provided as input.
key_value_map = type(out)()
for key, value in iterable:
key_value_map[key] = value
# Insert default values for non-existing keys.
for key in all_default_keys:
if not isinstance(key.default, Undefined) and \
key.schema not in key_value_map:
# A default value has been specified for this missing
# key, insert it.
key_value_map[key.schema] = key.default()
error = None
errors = []
for key, value in key_value_map.items():
key_path = path + [key]
remove_key = False
# Optimization. Validate against the matching key first, then fallback to the rest
relevant_candidates = itertools.chain(candidates_by_key.get(key, []), additional_candidates)
# compare each given key/value against all compiled key/values
# schema key, (compiled key, compiled value)
for skey, (ckey, cvalue) in relevant_candidates:
try:
new_key = ckey(key_path, key)
except er.Invalid as e:
if len(e.path) > len(key_path):
raise
if not error or len(e.path) > len(error.path):
error = e
continue
# Backtracking is not performed once a key is selected, so if
# the value is invalid we immediately throw an exception.
exception_errors = []
# check if the key is marked for removal
is_remove = new_key is Remove
try:
cval = cvalue(key_path, value)
# include if it's not marked for removal
if not is_remove:
out[new_key] = cval
else:
remove_key = True
continue
except er.MultipleInvalid as e:
exception_errors.extend(e.errors)
except er.Invalid as e:
exception_errors.append(e)
if exception_errors:
if is_remove or remove_key:
continue
for err in exception_errors:
if len(err.path) <= len(key_path):
err.error_type = invalid_msg
errors.append(err)
# If there is a validation error for a required
# key, this means that the key was provided.
# Discard the required key so it does not
# create an additional, noisy exception.
required_keys.discard(skey)
break
# Key and value okay, mark as found in case it was
# a Required() field.
required_keys.discard(skey)
break
else:
if remove_key:
# remove key
continue
elif self.extra == ALLOW_EXTRA:
out[key] = value
elif self.extra != REMOVE_EXTRA:
errors.append(er.Invalid('extra keys not allowed', key_path))
# else REMOVE_EXTRA: ignore the key so it's removed from output
# for any required keys left that weren't found and don't have defaults:
for key in required_keys:
msg = key.msg if hasattr(key, 'msg') and key.msg else 'required key not provided'
errors.append(er.RequiredFieldInvalid(msg, path + [key]))
if errors:
raise er.MultipleInvalid(errors)
return out
return validate_mapping
def _compile_object(self, schema):
"""Validate an object.
Has the same behavior as dictionary validator but work with object
attributes.
For example:
>>> class Structure(object):
... def __init__(self, one=None, three=None):
... self.one = one
... self.three = three
...
>>> validate = Schema(Object({'one': 'two', 'three': 'four'}, cls=Structure))
>>> with raises(er.MultipleInvalid, "not a valid value for object value @ data['one']"):
... validate(Structure(one='three'))
"""
base_validate = self._compile_mapping(
schema, invalid_msg='object value')
def validate_object(path, data):
if schema.cls is not UNDEFINED and not isinstance(data, schema.cls):
raise er.ObjectInvalid('expected a {0!r}'.format(schema.cls), path)
iterable = _iterate_object(data)
iterable = ifilter(lambda item: item[1] is not None, iterable)
out = base_validate(path, iterable, {})
return type(data)(**out)
return validate_object
def _compile_dict(self, schema):
"""Validate a dictionary.
A dictionary schema can contain a set of values, or at most one
validator function/type.
A dictionary schema will only validate a dictionary:
>>> validate = Schema({})
>>> with raises(er.MultipleInvalid, 'expected a dictionary'):
... validate([])
An invalid dictionary value:
>>> validate = Schema({'one': 'two', 'three': 'four'})
>>> with raises(er.MultipleInvalid, "not a valid value for dictionary value @ data['one']"):
... validate({'one': 'three'})
An invalid key:
>>> with raises(er.MultipleInvalid, "extra keys not allowed @ data['two']"):
... validate({'two': 'three'})
Validation function, in this case the "int" type:
>>> validate = Schema({'one': 'two', 'three': 'four', int: str})
Valid integer input:
>>> validate({10: 'twenty'})
{10: 'twenty'}
By default, a "type" in the schema (in this case "int") will be used
purely to validate that the corresponding value is of that type. It
will not Coerce the value:
>>> with raises(er.MultipleInvalid, "extra keys not allowed @ data['10']"):
... validate({'10': 'twenty'})
Wrap them in the Coerce() function to achieve this:
>>> from voluptuous import Coerce
>>> validate = Schema({'one': 'two', 'three': 'four',
... Coerce(int): str})
>>> validate({'10': 'twenty'})
{10: 'twenty'}
Custom message for required key
>>> validate = Schema({Required('one', 'required'): 'two'})
>>> with raises(er.MultipleInvalid, "required @ data['one']"):
... validate({})
(This is to avoid unexpected surprises.)
Multiple errors for nested field in a dict:
>>> validate = Schema({
... 'adict': {
... 'strfield': str,
... 'intfield': int
... }
... })
>>> try:
... validate({
... 'adict': {
... 'strfield': 123,
... 'intfield': 'one'
... }
... })
... except er.MultipleInvalid as e:
... print(sorted(str(i) for i in e.errors)) # doctest: +NORMALIZE_WHITESPACE
["expected int for dictionary value @ data['adict']['intfield']",
"expected str for dictionary value @ data['adict']['strfield']"]
"""
base_validate = self._compile_mapping(
schema, invalid_msg='dictionary value')
groups_of_exclusion = {}
groups_of_inclusion = {}
for node in schema:
if isinstance(node, Exclusive):
g = groups_of_exclusion.setdefault(node.group_of_exclusion, [])
g.append(node)
elif isinstance(node, Inclusive):
g = groups_of_inclusion.setdefault(node.group_of_inclusion, [])
g.append(node)
def validate_dict(path, data):
if not isinstance(data, dict):
raise er.DictInvalid('expected a dictionary', path)
errors = []
for label, group in groups_of_exclusion.items():
exists = False
for exclusive in group:
if exclusive.schema in data:
if exists:
msg = exclusive.msg if hasattr(exclusive, 'msg') and exclusive.msg else \
"two or more values in the same group of exclusion '%s'" % label
next_path = path + [VirtualPathComponent(label)]
errors.append(er.ExclusiveInvalid(msg, next_path))
break
exists = True
if errors:
raise er.MultipleInvalid(errors)
for label, group in groups_of_inclusion.items():
included = [node.schema in data for node in group]
if any(included) and not all(included):
msg = "some but not all values in the same group of inclusion '%s'" % label
for g in group:
if hasattr(g, 'msg') and g.msg:
msg = g.msg
break
next_path = path + [VirtualPathComponent(label)]
errors.append(er.InclusiveInvalid(msg, next_path))
break
if errors:
raise er.MultipleInvalid(errors)
out = data.__class__()
return base_validate(path, iteritems(data), out)
return validate_dict
def _compile_sequence(self, schema, seq_type):
"""Validate a sequence type.
This is a sequence of valid values or validators tried in order.
>>> validator = Schema(['one', 'two', int])
>>> validator(['one'])
['one']
>>> with raises(er.MultipleInvalid, 'expected int @ data[0]'):
... validator([3.5])
>>> validator([1])
[1]
"""
_compiled = [self._compile(s) for s in schema]
seq_type_name = seq_type.__name__
def validate_sequence(path, data):
if not isinstance(data, seq_type):
raise er.SequenceTypeInvalid('expected a %s' % seq_type_name, path)
# Empty seq schema, reject any data.
if not schema:
if data:
raise er.MultipleInvalid([
er.ValueInvalid('not a valid value', path if path else data)
])
return data
out = []
invalid = None
errors = []
index_path = UNDEFINED
for i, value in enumerate(data):
index_path = path + [i]
invalid = None
for validate in _compiled:
try:
cval = validate(index_path, value)
if cval is not Remove: # do not include Remove values
out.append(cval)
break
except er.Invalid as e:
if len(e.path) > len(index_path):
raise
invalid = e
else:
errors.append(invalid)
if errors:
raise er.MultipleInvalid(errors)
if _isnamedtuple(data):
return type(data)(*out)
else:
return type(data)(out)
return validate_sequence
def _compile_tuple(self, schema):
"""Validate a tuple.
A tuple is a sequence of valid values or validators tried in order.
>>> validator = Schema(('one', 'two', int))
>>> validator(('one',))
('one',)
>>> with raises(er.MultipleInvalid, 'expected int @ data[0]'):
... validator((3.5,))
>>> validator((1,))
(1,)
"""
return self._compile_sequence(schema, tuple)
def _compile_list(self, schema):
"""Validate a list.
A list is a sequence of valid values or validators tried in order.
>>> validator = Schema(['one', 'two', int])
>>> validator(['one'])
['one']
>>> with raises(er.MultipleInvalid, 'expected int @ data[0]'):
... validator([3.5])
>>> validator([1])
[1]
"""
return self._compile_sequence(schema, list)
def _compile_set(self, schema):
"""Validate a set.
A set is an unordered collection of unique elements.
>>> validator = Schema({int})
>>> validator(set([42])) == set([42])
True
>>> with raises(er.Invalid, 'expected a set'):
... validator(42)
>>> with raises(er.MultipleInvalid, 'invalid value in set'):
... validator(set(['a']))
"""
type_ = type(schema)
type_name = type_.__name__
def validate_set(path, data):
if not isinstance(data, type_):
raise er.Invalid('expected a %s' % type_name, path)
_compiled = [self._compile(s) for s in schema]
errors = []
for value in data:
for validate in _compiled:
try:
validate(path, value)
break
except er.Invalid:
pass
else:
invalid = er.Invalid('invalid value in %s' % type_name, path)
errors.append(invalid)
if errors:
raise er.MultipleInvalid(errors)
return data
return validate_set
def extend(self, schema, required=None, extra=None):
"""Create a new `Schema` by merging this and the provided `schema`.
Neither this `Schema` nor the provided `schema` are modified. The
resulting `Schema` inherits the `required` and `extra` parameters of
this, unless overridden.
Both schemas must be dictionary-based.
:param schema: dictionary to extend this `Schema` with
:param required: if set, overrides `required` of this `Schema`
:param extra: if set, overrides `extra` of this `Schema`
"""
assert type(self.schema) == dict and type(schema) == dict, 'Both schemas must be dictionary-based'
result = self.schema.copy()
# returns the key that may have been passed as an argument to Marker constructor
def key_literal(key):
return (key.schema if isinstance(key, Marker) else key)
# build a map that takes the key literals to the needed objects
# literal -> Required|Optional|literal
result_key_map = dict((key_literal(key), key) for key in result)
# for each item in the extension schema, replace duplicates
# or add new keys
for key, value in iteritems(schema):
# if the key is already in the dictionary, we need to replace it
# transform key to literal before checking presence
if key_literal(key) in result_key_map:
result_key = result_key_map[key_literal(key)]
result_value = result[result_key]
# if both are dictionaries, we need to extend recursively
# create the new extended sub schema, then remove the old key and add the new one
if type(result_value) == dict and type(value) == dict:
new_value = Schema(result_value).extend(value).schema
del result[result_key]
result[key] = new_value
# one or the other or both are not sub-schemas, simple replacement is fine
# remove old key and add new one
else:
del result[result_key]
result[key] = value
# key is new and can simply be added
else:
result[key] = value
# recompile and send old object
result_cls = type(self)
result_required = (required if required is not None else self.required)
result_extra = (extra if extra is not None else self.extra)
return result_cls(result, required=result_required, extra=result_extra)
def _compile_scalar(schema):
"""A scalar value.
The schema can either be a value or a type.
>>> _compile_scalar(int)([], 1)
1
>>> with raises(er.Invalid, 'expected float'):
... _compile_scalar(float)([], '1')
Callables have
>>> _compile_scalar(lambda v: float(v))([], '1')
1.0
As a convenience, ValueError's are trapped:
>>> with raises(er.Invalid, 'not a valid value'):
... _compile_scalar(lambda v: float(v))([], 'a')
"""
if inspect.isclass(schema):
def validate_instance(path, data):
if isinstance(data, schema):
return data
else:
msg = 'expected %s' % schema.__name__
raise er.TypeInvalid(msg, path)
return validate_instance
if callable(schema):
def validate_callable(path, data):
try:
return schema(data)
except ValueError:
raise er.ValueInvalid('not a valid value', path)
except er.Invalid as e:
e.prepend(path)
raise
return validate_callable
def validate_value(path, data):
if data != schema:
raise er.ScalarInvalid('not a valid value', path)
return data
return validate_value
def _compile_itemsort():
'''return sort function of mappings'''
def is_extra(key_):
return key_ is Extra
def is_remove(key_):
return isinstance(key_, Remove)
def is_marker(key_):
return isinstance(key_, Marker)
def is_type(key_):
return inspect.isclass(key_)
def is_callable(key_):
return callable(key_)
# priority list for map sorting (in order of checking)
# We want Extra to match last, because it's a catch-all. On the other hand,
# Remove markers should match first (since invalid values will not
# raise an Error, instead the validator will check if other schemas match
# the same value).
priority = [(1, is_remove), # Remove highest priority after values
(2, is_marker), # then other Markers
(4, is_type), # types/classes lowest before Extra
(3, is_callable), # callables after markers
(5, is_extra)] # Extra lowest priority
def item_priority(item_):
key_ = item_[0]
for i, check_ in priority:
if check_(key_):
return i
# values have hightest priorities
return 0
return item_priority
_sort_item = _compile_itemsort()
def _iterate_mapping_candidates(schema):
"""Iterate over schema in a meaningful order."""
# Without this, Extra might appear first in the iterator, and fail to
# validate a key even though it's a Required that has its own validation,
# generating a false positive.
return sorted(iteritems(schema), key=_sort_item)
def _iterate_object(obj):
"""Return iterator over object attributes. Respect objects with
defined __slots__.
"""
d = {}
try:
d = vars(obj)
except TypeError:
# maybe we have named tuple here?
if hasattr(obj, '_asdict'):
d = obj._asdict()
for item in iteritems(d):
yield item
try:
slots = obj.__slots__
except AttributeError:
pass
else:
for key in slots:
if key != '__dict__':
yield (key, getattr(obj, key))
class Msg(object):
"""Report a user-friendly message if a schema fails to validate.
>>> validate = Schema(
... Msg(['one', 'two', int],
... 'should be one of "one", "two" or an integer'))
>>> with raises(er.MultipleInvalid, 'should be one of "one", "two" or an integer'):
... validate(['three'])
Messages are only applied to invalid direct descendants of the schema:
>>> validate = Schema(Msg([['one', 'two', int]], 'not okay!'))
>>> with raises(er.MultipleInvalid, 'expected int @ data[0][0]'):
... validate([['three']])
The type which is thrown can be overridden but needs to be a subclass of Invalid
>>> with raises(er.SchemaError, 'Msg can only use subclases of Invalid as custom class'):
... validate = Schema(Msg([int], 'should be int', cls=KeyError))
If you do use a subclass of Invalid, that error will be thrown (wrapped in a MultipleInvalid)
>>> validate = Schema(Msg([['one', 'two', int]], 'not okay!', cls=er.RangeInvalid))
>>> try:
... validate(['three'])
... except er.MultipleInvalid as e:
... assert isinstance(e.errors[0], er.RangeInvalid)
"""
def __init__(self, schema, msg, cls=None):
if cls and not issubclass(cls, er.Invalid):
raise er.SchemaError("Msg can only use subclases of"
" Invalid as custom class")
self._schema = schema
self.schema = Schema(schema)
self.msg = msg
self.cls = cls
def __call__(self, v):
try:
return self.schema(v)
except er.Invalid as e:
if len(e.path) > 1:
raise e
else:
raise (self.cls or er.Invalid)(self.msg)
def __repr__(self):
return 'Msg(%s, %s, cls=%s)' % (self._schema, self.msg, self.cls)
class Object(dict):
"""Indicate that we should work with attributes, not keys."""
def __init__(self, schema, cls=UNDEFINED):
self.cls = cls
super(Object, self).__init__(schema)
class VirtualPathComponent(str):
def __str__(self):
return '<' + self + '>'
def __repr__(self):
return self.__str__()
# Markers.py
class Marker(object):
"""Mark nodes for special treatment."""
def __init__(self, schema_, msg=None, description=None):
self.schema = schema_
self._schema = Schema(schema_)
self.msg = msg
self.description = description
def __call__(self, v):
try:
return self._schema(v)
except er.Invalid as e:
if not self.msg or len(e.path) > 1:
raise
raise er.Invalid(self.msg)
def __str__(self):
return str(self.schema)
def __repr__(self):
return repr(self.schema)
def __lt__(self, other):
if isinstance(other, Marker):
return self.schema < other.schema
return self.schema < other
def __hash__(self):
return hash(self.schema)
def __eq__(self, other):
return self.schema == other
def __ne__(self, other):
return not(self.schema == other)
class Optional(Marker):
"""Mark a node in the schema as optional, and optionally provide a default
>>> schema = Schema({Optional('key'): str})
>>> schema({})
{}
>>> schema = Schema({Optional('key', default='value'): str})
>>> schema({})
{'key': 'value'}
>>> schema = Schema({Optional('key', default=list): list})
>>> schema({})
{'key': []}
If 'required' flag is set for an entire schema, optional keys aren't required
>>> schema = Schema({
... Optional('key'): str,
... 'key2': str
... }, required=True)
>>> schema({'key2':'value'})
{'key2': 'value'}
"""
def __init__(self, schema, msg=None, default=UNDEFINED, description=None):
super(Optional, self).__init__(schema, msg=msg,
description=description)
self.default = default_factory(default)
class Exclusive(Optional):
"""Mark a node in the schema as exclusive.
Exclusive keys inherited from Optional:
>>> schema = Schema({Exclusive('alpha', 'angles'): int, Exclusive('beta', 'angles'): int})
>>> schema({'alpha': 30})
{'alpha': 30}
Keys inside a same group of exclusion cannot be together, it only makes sense for dictionaries:
>>> with raises(er.MultipleInvalid, "two or more values in the same group of exclusion 'angles' @ data[<angles>]"):
... schema({'alpha': 30, 'beta': 45})
For example, API can provides multiple types of authentication, but only one works in the same time:
>>> msg = 'Please, use only one type of authentication at the same time.'
>>> schema = Schema({
... Exclusive('classic', 'auth', msg=msg):{
... Required('email'): basestring,
... Required('password'): basestring
... },
... Exclusive('internal', 'auth', msg=msg):{
... Required('secret_key'): basestring
... },
... Exclusive('social', 'auth', msg=msg):{
... Required('social_network'): basestring,
... Required('token'): basestring
... }
... })
>>> with raises(er.MultipleInvalid, "Please, use only one type of authentication at the same time. @ data[<auth>]"):
... schema({'classic': {'email': 'foo@example.com', 'password': 'bar'},
... 'social': {'social_network': 'barfoo', 'token': 'tEMp'}})
"""
def __init__(self, schema, group_of_exclusion, msg=None, description=None):
super(Exclusive, self).__init__(schema, msg=msg,
description=description)
self.group_of_exclusion = group_of_exclusion
class Inclusive(Optional):
""" Mark a node in the schema as inclusive.
Inclusive keys inherited from Optional:
>>> schema = Schema({
... Inclusive('filename', 'file'): str,
... Inclusive('mimetype', 'file'): str
... })
>>> data = {'filename': 'dog.jpg', 'mimetype': 'image/jpeg'}
>>> data == schema(data)
True
Keys inside a same group of inclusive must exist together, it only makes sense for dictionaries:
>>> with raises(er.MultipleInvalid, "some but not all values in the same group of inclusion 'file' @ data[<file>]"):
... schema({'filename': 'dog.jpg'})
If none of the keys in the group are present, it is accepted:
>>> schema({})
{}
For example, API can return 'height' and 'width' together, but not separately.
>>> msg = "Height and width must exist together"
>>> schema = Schema({
... Inclusive('height', 'size', msg=msg): int,
... Inclusive('width', 'size', msg=msg): int
... })
>>> with raises(er.MultipleInvalid, msg + " @ data[<size>]"):
... schema({'height': 100})
>>> with raises(er.MultipleInvalid, msg + " @ data[<size>]"):
... schema({'width': 100})
>>> data = {'height': 100, 'width': 100}
>>> data == schema(data)
True
"""
def __init__(self, schema, group_of_inclusion,
msg=None, description=None, default=UNDEFINED):
super(Inclusive, self).__init__(schema, msg=msg,
default=default,
description=description)
self.group_of_inclusion = group_of_inclusion
class Required(Marker):
"""Mark a node in the schema as being required, and optionally provide a default value.
>>> schema = Schema({Required('key'): str})
>>> with raises(er.MultipleInvalid, "required key not provided @ data['key']"):
... schema({})
>>> schema = Schema({Required('key', default='value'): str})
>>> schema({})
{'key': 'value'}
>>> schema = Schema({Required('key', default=list): list})
>>> schema({})
{'key': []}
"""
def __init__(self, schema, msg=None, default=UNDEFINED, description=None):
super(Required, self).__init__(schema, msg=msg,
description=description)
self.default = default_factory(default)
class Remove(Marker):
"""Mark a node in the schema to be removed and excluded from the validated
output. Keys that fail validation will not raise ``Invalid``. Instead, these
keys will be treated as extras.
>>> schema = Schema({str: int, Remove(int): str})
>>> with raises(er.MultipleInvalid, "extra keys not allowed @ data[1]"):
... schema({'keep': 1, 1: 1.0})
>>> schema({1: 'red', 'red': 1, 2: 'green'})
{'red': 1}
>>> schema = Schema([int, Remove(float), Extra])
>>> schema([1, 2, 3, 4.0, 5, 6.0, '7'])
[1, 2, 3, 5, '7']
"""
def __call__(self, v):
super(Remove, self).__call__(v)
return self.__class__
def __repr__(self):
return "Remove(%r)" % (self.schema,)
def __hash__(self):
return object.__hash__(self)
def message(default=None, cls=None):
"""Convenience decorator to allow functions to provide a message.
Set a default message:
>>> @message('not an integer')
... def isint(v):
... return int(v)
>>> validate = Schema(isint())
>>> with raises(er.MultipleInvalid, 'not an integer'):
... validate('a')
The message can be overridden on a per validator basis:
>>> validate = Schema(isint('bad'))
>>> with raises(er.MultipleInvalid, 'bad'):
... validate('a')
The class thrown too:
>>> class IntegerInvalid(er.Invalid): pass
>>> validate = Schema(isint('bad', clsoverride=IntegerInvalid))
>>> try:
... validate('a')
... except er.MultipleInvalid as e:
... assert isinstance(e.errors[0], IntegerInvalid)
"""
if cls and not issubclass(cls, er.Invalid):
raise er.SchemaError("message can only use subclases of Invalid as custom class")
def decorator(f):
@wraps(f)
def check(msg=None, clsoverride=None):
@wraps(f)
def wrapper(*args, **kwargs):
try:
return f(*args, **kwargs)
except ValueError:
raise (clsoverride or cls or er.ValueInvalid)(msg or default or 'invalid value')
return wrapper
return check
return decorator
def _args_to_dict(func, args):
"""Returns argument names as values as key-value pairs."""
if sys.version_info >= (3, 0):
arg_count = func.__code__.co_argcount
arg_names = func.__code__.co_varnames[:arg_count]
else:
arg_count = func.func_code.co_argcount
arg_names = func.func_code.co_varnames[:arg_count]
arg_value_list = list(args)
arguments = dict((arg_name, arg_value_list[i])
for i, arg_name in enumerate(arg_names)
if i < len(arg_value_list))
return arguments
def _merge_args_with_kwargs(args_dict, kwargs_dict):
"""Merge args with kwargs."""
ret = args_dict.copy()
ret.update(kwargs_dict)
return ret
def validate(*a, **kw):
"""Decorator for validating arguments of a function against a given schema.
Set restrictions for arguments:
>>> @validate(arg1=int, arg2=int)
... def foo(arg1, arg2):
... return arg1 * arg2
Set restriction for returned value:
>>> @validate(arg=int, __return__=int)
... def bar(arg1):
... return arg1 * 2
"""
RETURNS_KEY = '__return__'
def validate_schema_decorator(func):
returns_defined = False
returns = None
schema_args_dict = _args_to_dict(func, a)
schema_arguments = _merge_args_with_kwargs(schema_args_dict, kw)
if RETURNS_KEY in schema_arguments:
returns_defined = True
returns = schema_arguments[RETURNS_KEY]
del schema_arguments[RETURNS_KEY]
input_schema = (Schema(schema_arguments, extra=ALLOW_EXTRA)
if len(schema_arguments) != 0 else lambda x: x)
output_schema = Schema(returns) if returns_defined else lambda x: x
@wraps(func)
def func_wrapper(*args, **kwargs):
args_dict = _args_to_dict(func, args)
arguments = _merge_args_with_kwargs(args_dict, kwargs)
validated_arguments = input_schema(arguments)
output = func(**validated_arguments)
return output_schema(output)
return func_wrapper
return validate_schema_decorator
Reference in a new issue Copy permalink