
We are going to add a lot of configuration options related to providers, and there will be some duplication. Each Section may have a number of attributes that will act as defaults for labels, flavors, and images. Labels, Flavors, and Images may set those same values. Providers may override them. We need to validate each of these values in each of those places. Then we also need to serialize and deserialize them to share between the schedulers and launchers. The more modular and reusable the schemas are, the easier it will be to understand and maintain this. To that end, this change introduces a method of unifying the validation and deserialization schemas. This will allow us to use the same voluptuous schema to validate the YAML as well as set the attributes on the python object. It allows mutating the key names so that "foo-bar" in YAML is translated to "foo_bar" in python. It also allows us to set the python attribute to None if no value is provided without also allowing None in the YAML input. Change-Id: I11932d2d7da8ecd26010319db5be04c19d64756f
149 lines
4.5 KiB
Python
149 lines
4.5 KiB
Python
# Copyright 2024 Acme Gating, LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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# This adds some helpers that are useful for mutating hyphenated YAML
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# structures into underscored python dicts.
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import voluptuous as vs
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UNDEFINED = object()
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def assemble(*schemas):
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"""Merge any number of voluptuous schemas into a single schema. The
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input schemas must all be dictionary-based.
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"""
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ret = vs.Schema({})
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for x in schemas:
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ret = ret.extend(x.schema)
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return ret
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def discriminate(comparator):
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"""Chose among multiple schemas in a Union using a supplied comparator
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function. This does some extra work to find the interesting part
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of nested schemas. It follows nested schemas down and picks the
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first one of each. This makes it suitable for use with All
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combined with RequiredExclusive. This is intended to be passed to
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the discriminant argument of Union.
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The comparator is called with the following arguments:
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:param object val: The input data to be validated.
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:param Schema s: The first schema with values likely to be comparable.
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"""
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def disc(val, alt):
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ret = []
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for a in alt:
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s = a
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while hasattr(s, 'validators'):
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s = s.validators[0]
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while hasattr(s, 'schema'):
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s = s.schema
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if comparator(val, s):
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ret.append(a)
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return ret
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return disc
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class RequiredExclusive:
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"""A validator to require that at least one of a set of alternates is
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present. Use this in combination with Exclusive and All (since
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Exclusive doesn't imply Required). Example:
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All(
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{Exclusive(...), Exclusive(...)},
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RequiredExclusive(...)
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)
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"""
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def __init__(self, *attributes, msg=None):
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self.schema = vs.Schema({
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# Use the mutated names for the requirements
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vs.Required(
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vs.Any(*attributes),
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msg=msg,
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): object,
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object: object,
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})
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def __call__(self, v):
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return self.schema(v)
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class Required(vs.Required):
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"""Require an attribute and mutate its name
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This mutates the name of the attribute to lowercase it and replace
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hyphens with underscares. This enables the output of the
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validator to be used directly in setting python object attributes.
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"""
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def __init__(self, schema, default=UNDEFINED, output=None):
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if not isinstance(schema, str):
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raise Exception("Only strings are supported")
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super().__init__(schema, default=default)
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if output is None:
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output = str(schema).replace('-', '_').lower()
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self.output = output
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def __call__(self, data):
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# Superclass ensures that data==schema
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super().__call__(data)
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# Return our mutated form
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return self.output
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class Optional(vs.Optional):
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"""Mark an attribute optional and mutate its name
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This mutates the name of the attribute to lowercase it and replace
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hyphens with underscares. This enables the output of the
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validator to be used directly in setting python object attributes.
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This works in conjuction with Nullable.
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"""
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def __init__(self, schema, default=UNDEFINED, output=None):
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if not isinstance(schema, str):
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raise Exception("Only strings are supported")
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super().__init__(schema, default=default)
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if output is None:
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output = str(schema).replace('-', '_').lower()
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self.output = output
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def __call__(self, data):
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# Superclass ensures that data==schema
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super().__call__(data)
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# Return our mutated form
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return self.output
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class Nullable:
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"""Set the output value to None when no input is supplied.
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When used with Optional, if no input value is supplied, this will
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set the output to None without also accepting None as an input
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value.
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"""
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def __init__(self, schema):
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self.schema = vs.Schema(schema)
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def __call__(self, v):
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if v is UNDEFINED:
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return None
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return self.schema(v)
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