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from collections import defaultdict |
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from dataclasses import dataclass |
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from typing import DefaultDict, List, Optional, Set, Union |
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@dataclass |
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class Dependency: |
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"""Dependency class. |
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Args: |
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key: key of the dependency |
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value: required value of the dependency, None for empty condition |
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is_set: whether the dependency should be set, or not set |
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""" |
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key: str |
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value: Union[str, bool, int, None] = True |
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is_set: bool = True |
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def check(self, dependency_values: Optional[List[str]]) -> bool: |
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""" |
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Check if dependency is satisfied |
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Args: |
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dependency_values: list of dependency values |
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Returns: |
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True if the dependency is satisfied, False otherwise |
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""" |
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if dependency_values is None: |
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dependency_values = [] |
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if self.value is None and self.is_set and len(dependency_values): |
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return False |
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elif self.value is None and not self.is_set and not len(dependency_values): |
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return False |
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elif self.is_set and self.value not in dependency_values: |
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return False |
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elif ( |
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not self.is_set |
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and len([v for v in dependency_values if v != self.value]) == 0 |
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): |
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return False |
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return True |
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class Nesting: |
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""" |
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A tree-like structure to specify nested dependencies of key-value pairs |
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In detail it maps dependencies of key requiring any number of key:value pairs |
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Primarily useful for specifying nested dependencies of UI elements shown in Wave. |
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""" |
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def __init__(self): |
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self.dependencies: DefaultDict[str, List[Dependency]] = defaultdict(list) |
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self.triggers: Set[str] = set() |
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def add(self, keys: List[str], dependencies: List[Dependency]): |
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""" |
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Append dependencies (key:value) for a given key |
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Args: |
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keys: keys to add dependencies for |
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dependencies: key:value pairs to depend on |
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""" |
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if len(set(keys)) != len(keys): |
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raise ValueError("Nesting keys must be unique.") |
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for dependency in dependencies: |
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self.triggers.add(dependency.key) |
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for key in set(keys): |
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self.dependencies[key].append(dependency) |
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