# Copyright 2019 IBM Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, List, Optional, TypeVar, Union
undefined = Undefined()
T = TypeVar("T")
Option = Union[Undefined, T]
[docs]class Schema:
schema: Dict[str, Any]
def __init__(
self,
desc: Option[str] = undefined,
default: Option[Any] = undefined,
forOptimizer: bool = True,
):
self.schema: Dict[str, Any] = {}
if not isinstance(default, Undefined):
self.schema["default"] = default
if not isinstance(desc, Undefined):
self.schema["description"] = desc
if not forOptimizer:
self.schema["forOptimizer"] = forOptimizer
[docs] def set(self, prop: str, value: Option[Any]):
if not isinstance(value, Undefined):
self.schema[prop] = value
# Base Type
[docs]class Bool(Schema):
def __init__(
self,
desc: Option[str] = undefined,
default: Option[bool] = undefined,
forOptimizer: bool = True,
):
super().__init__(desc, default, forOptimizer)
self.set("type", "boolean")
[docs]class Enum(Schema):
def __init__(
self,
values: Optional[List[Any]] = None,
desc: Option[str] = undefined,
default: Option[Any] = undefined,
forOptimizer: bool = True,
):
super().__init__(desc, default, forOptimizer)
if values is None:
values = []
self.set("enum", values)
[docs]class Float(Schema):
def __init__(
self,
desc: Option[str] = undefined,
default: Option[float] = undefined,
forOptimizer: bool = True,
minimum: Option[float] = undefined,
exclusiveMinimum: Option[bool] = undefined,
minimumForOptimizer: Option[float] = undefined,
exclusiveMinimumForOptimizer: Option[bool] = undefined,
maximum: Option[float] = undefined,
exclusiveMaximum: Option[bool] = undefined,
maximumForOptimizer: Option[float] = undefined,
exclusiveMaximumForOptimizer: Option[bool] = undefined,
distribution: Option[str] = undefined,
):
super().__init__(desc, default, forOptimizer)
self.set("type", "number")
self.set("minimum", minimum)
self.set("exclusiveMinimum", exclusiveMinimum)
self.set("minimumForOptimizer", minimumForOptimizer)
self.set("exclusiveMinimumForOptimizer", exclusiveMinimumForOptimizer)
self.set("maximum", maximum)
self.set("exclusiveMaximum", exclusiveMaximum)
self.set("maximumForOptimizer", maximumForOptimizer)
self.set("exclusiveMaximumForOptimizer", exclusiveMaximumForOptimizer)
self.set("distribution", distribution)
[docs]class Int(Schema):
def __init__(
self,
desc: Option[str] = undefined,
default: Option[int] = undefined,
forOptimizer: bool = True,
minimum: Option[int] = undefined,
exclusiveMinimum: Option[bool] = undefined,
minimumForOptimizer: Option[int] = undefined,
exclusiveMinimumForOptimizer: Option[bool] = undefined,
maximum: Option[int] = undefined,
exclusiveMaximum: Option[bool] = undefined,
maximumForOptimizer: Option[int] = undefined,
exclusiveMaximumForOptimizer: Option[bool] = undefined,
distribution: Option[str] = undefined,
laleMaximum: Option[str] = undefined,
):
super().__init__(desc, default, forOptimizer)
self.set("type", "integer")
self.set("minimum", minimum)
self.set("exclusiveMinimum", exclusiveMinimum)
self.set("minimumForOptimizer", minimumForOptimizer)
self.set("maximum", maximum)
self.set("exclusiveMaximum", exclusiveMaximum)
self.set("exclusiveMinimumForOptimizer", exclusiveMinimumForOptimizer)
self.set("maximumForOptimizer", maximumForOptimizer)
self.set("exclusiveMaximumForOptimizer", exclusiveMaximumForOptimizer)
self.set("distribution", distribution)
self.set("laleMaximum", laleMaximum)
[docs]class Null(Schema):
def __init__(self, desc: Option[str] = undefined, forOptimizer: bool = True):
super().__init__(desc=desc, forOptimizer=forOptimizer)
self.set("enum", [None])
[docs]class Not(Schema):
def __init__(self, body: Schema):
super().__init__()
self.schema = {"not": body.schema}
[docs]class JSON(Schema):
def __init__(self, body: Dict[str, Any]):
super().__init__()
self.schema = body
# Combinator
[docs]class AnyOf(Schema):
def __init__(
self,
types: Optional[List[Schema]] = None,
desc: Option[str] = undefined,
default: Option[Any] = undefined,
forOptimizer: bool = True,
):
super().__init__(desc, default, forOptimizer)
if types is None:
types = []
self.set("anyOf", [t.schema for t in types])
[docs]class AllOf(Schema):
def __init__(
self,
types: Optional[List[Schema]] = None,
desc: Option[str] = undefined,
default: Option[Any] = undefined,
):
super().__init__(desc, default)
if types is None:
types = []
self.set("allOf", [t.schema for t in types])
[docs]class Array(Schema):
def __init__(
self,
items: Schema,
desc: Option[str] = undefined,
default: Option[List[Any]] = undefined,
forOptimizer: bool = True,
minItems: Option[int] = undefined,
minItemsForOptimizer: Option[int] = undefined,
maxItems: Option[int] = undefined,
maxItemsForOptimizer: Option[int] = undefined,
laleType: Option[str] = undefined,
):
super().__init__(desc, default, forOptimizer)
self.set("type", "array")
self.set("items", items.schema)
self.set("minItems", minItems)
self.set("minItemsForOptimizer", minItemsForOptimizer)
self.set("maxItems", maxItems)
self.set("maxItemsForOptimizer", maxItemsForOptimizer)
self.set("laleType", laleType)
[docs]class Object(Schema):
def __init__(
self,
default: Option[Any] = undefined,
desc: Option[str] = undefined,
forOptimizer: bool = True,
required: Option[List[str]] = undefined,
additionalProperties: Option[bool] = undefined,
**kwargs: Schema
):
super().__init__(desc, default, forOptimizer)
self.set("type", "object")
self.set("required", required)
self.set("additionalProperties", additionalProperties)
self.set("properties", {k: p.schema for (k, p) in kwargs.items()})
[docs]class String(Schema):
def __init__(
self,
desc: Option[str] = undefined,
default: Option[str] = undefined,
forOptimizer: bool = False,
):
super().__init__(desc, default, forOptimizer)
self.set("type", "string")