def existing_image(filename: str) -> str:
if not filename:
raise SchemaError('No file given')
if not os.path.exists(filename):
raise SchemaError('File "{0}" does not exist'.format(filename))
ensure_opencv_filename(filename)
return os.path.realpath(filename)
def plugin_list(value):
"""Turn a space-delimited series of plugin names into a ListAndAll of
Plugins.
Never return the core plugin.
"""
if not isinstance(value, basestring):
# Probably can't happen
raise SchemaError(
'"%s" is neither * nor a whitespace-delimited list '
'of plugin names.' % (value, ), None)
plugins = all_plugins_but_core()
names = value.strip().split()
is_all = names == ['*']
if is_all:
names = plugins.keys()
try:
ret = ListAndAll([plugins[name] for name in names])
ret.is_all = is_all
return ret
except KeyError:
raise SchemaError(
'Never heard of plugin "%s". I\'ve heard of '
'these: %s.' % (name, ', '.join(plugins.keys())), None)
def validate_pai_config_path(self, experiment_config):
'''validate paiConfigPath field'''
if experiment_config.get('trainingServicePlatform') == 'pai':
if experiment_config.get('trial', {}).get('paiConfigPath'):
# validate commands
pai_config = get_yml_content(
experiment_config['trial']['paiConfigPath'])
taskRoles_dict = pai_config.get('taskRoles')
if not taskRoles_dict:
raise SchemaError(
'Please set taskRoles in paiConfigPath config file!')
else:
pai_trial_fields_required_list = [
'image', 'paiStorageConfigName', 'command'
]
for trial_field in pai_trial_fields_required_list:
if experiment_config['trial'].get(trial_field) is None:
raise SchemaError(
'Please set {0} in trial configuration,\
or set additional pai configuration file path in paiConfigPath!'
.format(trial_field))
pai_resource_fields_required_list = [
'gpuNum', 'cpuNum', 'memoryMB'
]
for required_field in pai_resource_fields_required_list:
if experiment_config['trial'].get(required_field) is None and \
experiment_config['paiConfig'].get(required_field) is None:
raise SchemaError(
'Please set {0} in trial or paiConfig configuration,\
or set additional pai configuration file path in paiConfigPath!'
.format(required_field))
def __call__(self, parent_schema, series, index):
if self.element_wise:
val_result = series.map(self.fn)
if val_result.all():
return True
raise SchemaError(
self.vectorized_error_message(parent_schema, index,
series[~val_result]))
else:
# series-wise validator can return either a boolean or a
# pd.Series of booleans.
val_result = self.fn(series)
if isinstance(val_result, pd.Series):
if not val_result.dtype == PandasDtype.Bool.value:
raise TypeError(
"validator %d: %s must return bool or Series of type "
"bool, found %s" %
(index, self.fn.__name__, val_result.dtype))
if val_result.all():
return True
raise SchemaError(
self.vectorized_error_message(parent_schema, index,
series[~val_result]))
else:
if val_result:
return True
else:
raise SchemaError(
"series did not pass series validator %d: %s" %
(index, self.error_message))
def validate_self_content(keypairs, datadict):
"""Validate a dict by comparing keypairs.
Args:
keypairs(obj): Iterable of tuples strings with len==2
datadict(dict): a dict
Returns:
None: if all entries are equal.
Raises:
SchemaError: if any key pair is different.
"""
datakeys = set(datadict.keys())
reqkeys = {y for x in keypairs for y in x}
missing = not reqkeys.issubset(datakeys)
if missing:
errmsg = ("Content validation failed.\n\
Cannot compare missing keys: %s " % (missing))
raise SchemaError(errmsg)
for k1, k2 in keypairs:
v1 = datadict[k1]
v2 = datadict[k2]
if v1 != v2:
errmsg = ("Content validation failed.\n\
(d[%s] = %s) != (d[%s] = %s)" % (k1, v1, k2, v2))
raise SchemaError(errmsg)
def __call__(self, series):
"""Validate a series."""
_dtype = self._pandas_dtype if isinstance(self._pandas_dtype, str) \
else self._pandas_dtype.value
if self._nullable:
series = series.dropna()
if (_dtype == Int.value):
_dtype = Float.value
if (series.astype(_dtype) != series).any():
# in case where dtype is meant to be int, make sure that
# casting to int results in the same values.
raise SchemaError(
"after dropping null values, expected series values "
"to be int, found: %s" % set(series))
else:
nulls = series.isnull()
if nulls.sum() > 0:
raise SchemaError(
"non-nullable series contains null values: %s" %
series[nulls].head(N_FAILURE_CASES).to_dict())
type_val_result = series.dtype == _dtype
if not type_val_result:
raise SchemaError(
"expected series '%s' to have type %s, got %s" %
(series.name, self._pandas_dtype.value, series.dtype))
check_results = []
for i, check in enumerate(self._checks):
check_results.append(check(self, series, i))
return all([type_val_result] + check_results)
def validate(self, x):
if not (isinstance(x, list) or isinstance(x, tuple)
or isinstance(x, numpy.ndarray)):
raise SchemaError("Sequence is not list, tuple or numpy array", [])
if isinstance(x, numpy.ndarray):
if x.dtype.kind != "f":
raise SchemaError(
"Array dtype must be float, "
"but was {}".format(x.dtype), [])
x = x.ravel()
if len(x) == 0:
raise ValueError("Expecting a non-empty sequence but "
"got {}".format(x))
if self.size is not None and len(x) != self.size:
raise SchemaError(
"Expecting sequence length {} but got "
"{}".format(self.size, len(x)), [])
if not isinstance(x, numpy.ndarray):
for value in x:
if not isinstance(value, (int, float)):
raise SchemaError(
"Values in sequence are expected to be "
"numeric", [])
x = numpy.array(x, dtype=float)
return x
def validate(self, value):
if not is_integer(value) and not is_float(value):
raise SchemaError(
"Salary has unexpected type {t}".format(t=type(value)))
elif value <= 10000:
raise SchemaError("Salary {s} is too low".format(s=value))
elif value >= 99999.9:
raise SchemaError("Salary {s} is too high".format(s=value))
def validate(self, data):
try:
return self._callable(data)
except SchemaError as x:
raise SchemaError([None] + x.autos, [self._error] + x.errors)
except BaseException as x:
f = self._callable.__name__
raise SchemaError('%s(%r) raised %r' % (f, data, x), self._error)
def validate(self, x):
if not isinstance(x, tuple):
raise SchemaError("Expecting tuple, got {}".format(type(x)), [])
if len(x) != self.N:
raise SchemaError(
"Expecting a tuple of size {}, but got".format(self.N, len(x)),
[])
return tuple(schema.validate(y) for y, schema in zip(x, self.tt))
def _validate_endpoint_list(value):
if value is None:
raise SchemaError('Invalid configuration: \'currentlist\' is not set')
if not isinstance(value, list):
raise SchemaError(
'Invalid configuration: \'currentlist\' is not a list of items')
for item in value:
_validate_valid_url(item, caller="'currentlist' items")
return True
def validate(self, data):
x = SchemaError([], [])
for s in [Schema(s, error=self._error) for s in self._args]:
try:
return s.validate(data)
except SchemaError as _x:
x = _x
raise SchemaError(['%r did not validate %r' % (self, data)] + x.autos,
[self._error] + x.errors)
def validate_tuner_adivosr_assessor(self, experiment_config):
if experiment_config.get('advisor'):
if experiment_config.get('assessor') or experiment_config.get('tuner'):
raise SchemaError('advisor could not be set with assessor or tuner simultaneously!')
self.validate_annotation_content(experiment_config, 'advisor', 'builtinAdvisorName')
else:
if not experiment_config.get('tuner'):
raise SchemaError('Please provide tuner spec!')
self.validate_annotation_content(experiment_config, 'tuner', 'builtinTunerName')
def validate(self, ets_id):
try:
ets_id = int(ets_id)
if ets_id not in self.valid_ets_ids:
raise SchemaError('entity-type selection %s not valid' % ets_id)
return ets_id
except Exception:
raise SchemaError('entity-type selection %s not valid' % ets_id)
def parse_time(time):
'''Change the time to seconds'''
unit = time[-1]
if unit not in ['s', 'm', 'h', 'd']:
raise SchemaError('the unit of time could only from {s, m, h, d}')
time = time[:-1]
if not time.isdigit():
raise SchemaError('time format error!')
parse_dict = {'s': 1, 'm': 60, 'h': 3600, 'd': 86400}
return int(time) * parse_dict[unit]
def _validate_valid_url(value, caller):
if value is None:
raise SchemaError(
'Invalid configuration: {} is not set'.format(caller))
if not isinstance(value, str):
raise SchemaError(
'Invalid configuration: {} must be strings'.format(caller))
if not valid_url_re.search(value):
raise SchemaError(
'Invalid configuration: {} must be a valid url'.format(caller))
return value
def _validate_mode(value):
if value is None:
raise SchemaError('Invalid configuration: \'MODE\' is not set')
if not isinstance(value, str):
raise SchemaError('Invalid configuration: \'MODE\' must be a string')
allowed_modes = {"PRIVATE", "PUBLIC [EU]", "PUBLIC [NAM]", "PUBLIC [APJC]"}
if value.upper() not in allowed_modes:
raise SchemaError(
'Invalid configuration: \'MODE\' must be one of the following {}'.
format(allowed_modes))
return value.upper()
def validate_search_space_content(self, experiment_config):
'''Validate searchspace content,
if the searchspace file is not json format or its values does not contain _type and _value which must be specified,
it will not be a valid searchspace file'''
try:
search_space_content = json.load(open(experiment_config.get('searchSpacePath'), 'r'))
for value in search_space_content.values():
if not value.get('_type') or not value.get('_value'):
raise SchemaError('please use _type and _value to specify searchspace!')
except Exception as e:
raise SchemaError('searchspace file is not a valid json format! ' + str(e))
def validate(self, x):
if not isinstance(x, (list, set, tuple)):
raise SchemaError("Sequence is not list, tuple nor set", [])
if x:
if self.elem_type:
elem_type = self.elem_type
else:
elem_type = self.infer_type_from_data(x)
if not all(isinstance(x_i, elem_type) for x_i in x):
msg = "Expecting all elements to be {}".format(self.elem_type)
raise SchemaError(msg, [])
return x
def check_institution(d):
"""If the agreement is institution, then we have to have an institution."""
if d['agreement'] == 'institution':
if 'institution' in d:
if d['institution'] not in ALL_ORGS:
raise SchemaError(
"Institution {!r} isn't in orgs.yaml: {}".format(
d['institution'], d))
if d['agreement'] == 'none':
if 'institution' in d:
raise SchemaError("No-agreement should have no institution")
return True
def validate_class_args(self, class_args, algo_type, builtin_name):
if not builtin_name or not class_args:
return
meta = get_builtin_algo_meta(algo_type+'s', builtin_name)
if meta and 'accept_class_args' in meta and meta['accept_class_args'] == False:
raise SchemaError('classArgs is not allowed.')
validator = create_validator_instance(algo_type+'s', builtin_name)
if validator:
try:
validator.validate_class_args(**class_args)
except Exception as e:
raise SchemaError(str(e))
def validate_class_args(self, class_args, algo_type, builtin_name):
if not builtin_name or not class_args:
return
meta = get_registered_algo_meta(builtin_name, algo_type+'s')
if meta and 'acceptClassArgs' in meta and meta['acceptClassArgs'] == False:
raise SchemaError('classArgs is not allowed.')
logging.getLogger('nni.protocol').setLevel(logging.ERROR) # we know IPC is not there, don't complain
validator = create_validator_instance(algo_type+'s', builtin_name)
if validator:
try:
validator.validate_class_args(**class_args)
except Exception as e:
raise SchemaError(str(e))
def validate_exclude_quant_types_quant_bits(data):
if not ('exclude' in data or
('quant_types' in data and 'quant_bits' in data)):
raise SchemaError(
'Either (quant_types and quant_bits) or exclude must be specified.'
)
return True
def get_validator(event_type: str) -> Tuple[str, callable]:
if event_type in TASK_EVENT_TYPES:
return "task", CompiledTaskEventSchema
elif event_type in WORKER_EVENT_TYPES:
return "worker", CompiledWorkerEventSchema
else:
raise SchemaError(f"{event_type} is not a valid celery event type!")
def __init__(self, value):
value = float(value)
if not 0 < value < 100:
raise SchemaError("Invalid percentile value")
self.value = value
def json_path(txt):
try:
logging.debug("validating as json path - '%s'" % txt)
return jsonpath_ng.parse(txt)
except Exception as e:
logging.error("Bad JsonPath format: '%s'" % txt)
raise SchemaError(['Bad JsonPath format: %s' % txt], str(e))
def validate(self, *args, **kwargs):
try:
item = self.validation_schema.validate(self.to_item())
return Currency(self.mongo, **item)
except SchemaError:
raise SchemaError(
'Error while validating data or code already exists')
def migrate(settings: dict) -> dict:
"""
Migration of the settings ``settings`` to version V3.3.1 settings
:param settings: The settings dict to migrate
:return: The migrated dict
"""
# rename keys and update their value
# add missing keys
# name - value pairs
missing_keys = {
'auto_migrate_settings': True,
'ldap_tls_cacertdir': "",
'ldap_tls_reqcert': "hard",
'ldap_tls_cipher_suite': "",
'bootloaders_shim_folder': "@@shim_folder@@",
'bootloaders_shim_file': "@@shim_file@@",
'bootloaders_ipxe_folder': "@@ipxe_folder@@",
'syslinux_memdisk_folder': "@@memdisk_folder@@",
'syslinux_pxelinux_folder': "@@pxelinux_folder@@",
}
for (key, value) in missing_keys.items():
new_setting = helper.Setting(key, value)
helper.key_add(new_setting, settings)
# delete removed keys
if not validate(settings):
raise SchemaError("V3.3.1: Schema error while validating")
return normalize(settings)
def validate_meta(meta=None, hard_validation=False):
"""
Validate meta data.
:param meta:
Meta data.
:type meta: dict
:param hard_validation:
Add extra data validations.
:type hard_validation: bool
:return:
Validated meta data.
:rtype: dict
"""
i, e = _validate_base_with_schema(meta or {}, depth=2)
if hard_validation:
from schema import SchemaError
from .hard import _hard_validation
for k, v in sorted(sh.stack_nested_keys(i, depth=1)):
for c, msg in _hard_validation(v, 'meta'):
sh.get_nested_dicts(e, *k)[c] = SchemaError([], [msg])
if _log_errors_msg(e):
return sh.NONE
return i
def test_skelebot_schema_error(self, mock_yaml, exit_mock, print_mock):
mock_yaml.side_effect = SchemaError("Validation Failed")
sb.main()
print_mock.assert_called_once_with(Fore.RED + "ERROR" + Style.RESET_ALL + " | skelebot.yaml | Validation Failed")
exit_mock.assert_called_once_with(1)