24024
af7c11d7f9
- 新增图像生成接口，支持试用、积分和自定义API Key模式 - 实现生成图片结果异步上传至MinIO存储，带重试机制 - 优化积分预扣除和异常退还逻辑，保障用户积分准确 - 添加获取生成历史记录接口，支持时间范围和分页 - 提供本地字典配置接口，支持模型、比例、提示模板和尺寸 - 实现图片批量上传接口，支持S3兼容对象存储 feat(admin): 增加管理员角色管理与权限分配接口 - 实现角色列表查询、角色创建、更新及删除功能 - 增加权限列表查询接口 - 实现用户角色分配接口，便于统一管理用户权限 - 增加系统字典增删查改接口，支持分类过滤和排序 - 权限控制全面覆盖管理接口，保证安全访问 feat(auth): 完善用户登录注册及权限相关接口与页面 - 实现手机号验证码发送及校验功能，保障注册安全 - 支持手机号注册、登录及退出接口，集成日志记录 - 增加修改密码功能，验证原密码后更新 - 提供动态导航菜单接口，基于权限展示不同菜单 - 实现管理界面路由及日志、角色、字典管理页面访问权限控制 - 添加系统日志查询接口，支持关键词和等级筛选 feat(app): 初始化Flask应用并配置蓝图与数据库 - 创建应用程序工厂，加载配置，初始化数据库和Redis客户端 - 注册认证、API及管理员蓝图，整合路由 - 根路由渲染主页模板 - 应用上下文中自动创建数据库表，保证运行环境准备完毕 feat(database): 提供数据库创建与迁移支持脚本 - 新增数据库创建脚本，支持自动检测是否已存在 - 添加数据库表初始化脚本，支持创建和删除所有表 - 实现RBAC权限初始化，包含基础权限和角色创建 - 新增字段手动修复脚本，添加用户API Key和积分字段 - 强制迁移脚本支持清理连接和修复表结构，初始化默认数据及角色分配 feat(config): 新增系统配置参数 - 配置数据库、Redis、Session和MinIO相关参数 - 添加AI接口地址及试用Key配置 - 集成阿里云短信服务配置及开发模式相关参数 feat(extensions): 初始化数据库、Redis和MinIO客户端 - 创建全局SQLAlchemy数据库实例和Redis客户端 - 配置基于boto3的MinIO兼容S3客户端 chore(logs): 添加示例系统日志文件 - 记录用户请求、验证码发送成功与失败的日志信息
520 lines
17 KiB
Python
520 lines
17 KiB
Python
# dialects/postgresql/array.py
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# Copyright (C) 2005-2025 the SQLAlchemy authors and contributors
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# <see AUTHORS file>
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#
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# This module is part of SQLAlchemy and is released under
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# the MIT License: https://www.opensource.org/licenses/mit-license.php
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from __future__ import annotations
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import re
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from typing import Any as typing_Any
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from typing import Iterable
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from typing import Optional
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from typing import Sequence
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from typing import TYPE_CHECKING
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from typing import TypeVar
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from typing import Union
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from .operators import CONTAINED_BY
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from .operators import CONTAINS
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from .operators import OVERLAP
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from ... import types as sqltypes
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from ... import util
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from ...sql import expression
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from ...sql import operators
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from ...sql.visitors import InternalTraversal
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if TYPE_CHECKING:
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from ...engine.interfaces import Dialect
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from ...sql._typing import _ColumnExpressionArgument
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from ...sql._typing import _TypeEngineArgument
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from ...sql.elements import ColumnElement
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from ...sql.elements import Grouping
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from ...sql.expression import BindParameter
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from ...sql.operators import OperatorType
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from ...sql.selectable import _SelectIterable
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from ...sql.type_api import _BindProcessorType
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from ...sql.type_api import _LiteralProcessorType
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from ...sql.type_api import _ResultProcessorType
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from ...sql.type_api import TypeEngine
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from ...sql.visitors import _TraverseInternalsType
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from ...util.typing import Self
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_T = TypeVar("_T", bound=typing_Any)
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_CT = TypeVar("_CT", bound=typing_Any)
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def Any(
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other: typing_Any,
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arrexpr: _ColumnExpressionArgument[_T],
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operator: OperatorType = operators.eq,
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) -> ColumnElement[bool]:
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"""A synonym for the ARRAY-level :meth:`.ARRAY.Comparator.any` method.
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See that method for details.
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"""
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return arrexpr.any(other, operator) # type: ignore[no-any-return, union-attr] # noqa: E501
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def All(
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other: typing_Any,
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arrexpr: _ColumnExpressionArgument[_T],
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operator: OperatorType = operators.eq,
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) -> ColumnElement[bool]:
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"""A synonym for the ARRAY-level :meth:`.ARRAY.Comparator.all` method.
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See that method for details.
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"""
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return arrexpr.all(other, operator) # type: ignore[no-any-return, union-attr] # noqa: E501
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class array(expression.ExpressionClauseList[_T]):
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"""A PostgreSQL ARRAY literal.
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This is used to produce ARRAY literals in SQL expressions, e.g.::
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from sqlalchemy.dialects.postgresql import array
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from sqlalchemy.dialects import postgresql
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from sqlalchemy import select, func
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stmt = select(array([1, 2]) + array([3, 4, 5]))
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print(stmt.compile(dialect=postgresql.dialect()))
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Produces the SQL:
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.. sourcecode:: sql
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SELECT ARRAY[%(param_1)s, %(param_2)s] ||
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ARRAY[%(param_3)s, %(param_4)s, %(param_5)s]) AS anon_1
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An instance of :class:`.array` will always have the datatype
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:class:`_types.ARRAY`. The "inner" type of the array is inferred from the
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values present, unless the :paramref:`_postgresql.array.type_` keyword
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argument is passed::
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array(["foo", "bar"], type_=CHAR)
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When constructing an empty array, the :paramref:`_postgresql.array.type_`
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argument is particularly important as PostgreSQL server typically requires
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a cast to be rendered for the inner type in order to render an empty array.
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SQLAlchemy's compilation for the empty array will produce this cast so
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that::
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stmt = array([], type_=Integer)
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print(stmt.compile(dialect=postgresql.dialect()))
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Produces:
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.. sourcecode:: sql
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ARRAY[]::INTEGER[]
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As required by PostgreSQL for empty arrays.
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.. versionadded:: 2.0.40 added support to render empty PostgreSQL array
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literals with a required cast.
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Multidimensional arrays are produced by nesting :class:`.array` constructs.
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The dimensionality of the final :class:`_types.ARRAY`
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type is calculated by
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recursively adding the dimensions of the inner :class:`_types.ARRAY`
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type::
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stmt = select(
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array(
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[array([1, 2]), array([3, 4]), array([column("q"), column("x")])]
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)
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)
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print(stmt.compile(dialect=postgresql.dialect()))
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Produces:
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.. sourcecode:: sql
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SELECT ARRAY[
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ARRAY[%(param_1)s, %(param_2)s],
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ARRAY[%(param_3)s, %(param_4)s],
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ARRAY[q, x]
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] AS anon_1
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.. versionadded:: 1.3.6 added support for multidimensional array literals
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.. seealso::
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:class:`_postgresql.ARRAY`
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""" # noqa: E501
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__visit_name__ = "array"
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stringify_dialect = "postgresql"
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_traverse_internals: _TraverseInternalsType = [
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("clauses", InternalTraversal.dp_clauseelement_tuple),
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("type", InternalTraversal.dp_type),
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]
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def __init__(
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self,
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clauses: Iterable[_T],
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*,
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type_: Optional[_TypeEngineArgument[_T]] = None,
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**kw: typing_Any,
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):
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r"""Construct an ARRAY literal.
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:param clauses: iterable, such as a list, containing elements to be
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rendered in the array
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:param type\_: optional type. If omitted, the type is inferred
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from the contents of the array.
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"""
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super().__init__(operators.comma_op, *clauses, **kw)
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main_type = (
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type_
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if type_ is not None
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else self.clauses[0].type if self.clauses else sqltypes.NULLTYPE
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)
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if isinstance(main_type, ARRAY):
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self.type = ARRAY(
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main_type.item_type,
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dimensions=(
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main_type.dimensions + 1
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if main_type.dimensions is not None
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else 2
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),
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) # type: ignore[assignment]
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else:
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self.type = ARRAY(main_type) # type: ignore[assignment]
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@property
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def _select_iterable(self) -> _SelectIterable:
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return (self,)
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def _bind_param(
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self,
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operator: OperatorType,
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obj: typing_Any,
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type_: Optional[TypeEngine[_T]] = None,
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_assume_scalar: bool = False,
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) -> BindParameter[_T]:
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if _assume_scalar or operator is operators.getitem:
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return expression.BindParameter(
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None,
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obj,
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_compared_to_operator=operator,
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type_=type_,
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_compared_to_type=self.type,
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unique=True,
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)
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else:
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return array(
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[
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self._bind_param(
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operator, o, _assume_scalar=True, type_=type_
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)
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for o in obj
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]
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) # type: ignore[return-value]
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def self_group(
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self, against: Optional[OperatorType] = None
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) -> Union[Self, Grouping[_T]]:
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if against in (operators.any_op, operators.all_op, operators.getitem):
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return expression.Grouping(self)
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else:
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return self
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class ARRAY(sqltypes.ARRAY[_T]):
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"""PostgreSQL ARRAY type.
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The :class:`_postgresql.ARRAY` type is constructed in the same way
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as the core :class:`_types.ARRAY` type; a member type is required, and a
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number of dimensions is recommended if the type is to be used for more
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than one dimension::
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from sqlalchemy.dialects import postgresql
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mytable = Table(
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"mytable",
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metadata,
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Column("data", postgresql.ARRAY(Integer, dimensions=2)),
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)
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The :class:`_postgresql.ARRAY` type provides all operations defined on the
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core :class:`_types.ARRAY` type, including support for "dimensions",
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indexed access, and simple matching such as
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:meth:`.types.ARRAY.Comparator.any` and
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:meth:`.types.ARRAY.Comparator.all`. :class:`_postgresql.ARRAY`
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class also
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provides PostgreSQL-specific methods for containment operations, including
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:meth:`.postgresql.ARRAY.Comparator.contains`
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:meth:`.postgresql.ARRAY.Comparator.contained_by`, and
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:meth:`.postgresql.ARRAY.Comparator.overlap`, e.g.::
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mytable.c.data.contains([1, 2])
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Indexed access is one-based by default, to match that of PostgreSQL;
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for zero-based indexed access, set
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:paramref:`_postgresql.ARRAY.zero_indexes`.
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Additionally, the :class:`_postgresql.ARRAY`
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type does not work directly in
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conjunction with the :class:`.ENUM` type. For a workaround, see the
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special type at :ref:`postgresql_array_of_enum`.
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.. container:: topic
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**Detecting Changes in ARRAY columns when using the ORM**
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The :class:`_postgresql.ARRAY` type, when used with the SQLAlchemy ORM,
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does not detect in-place mutations to the array. In order to detect
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these, the :mod:`sqlalchemy.ext.mutable` extension must be used, using
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the :class:`.MutableList` class::
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from sqlalchemy.dialects.postgresql import ARRAY
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from sqlalchemy.ext.mutable import MutableList
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class SomeOrmClass(Base):
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# ...
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data = Column(MutableList.as_mutable(ARRAY(Integer)))
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This extension will allow "in-place" changes such to the array
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such as ``.append()`` to produce events which will be detected by the
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unit of work. Note that changes to elements **inside** the array,
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including subarrays that are mutated in place, are **not** detected.
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Alternatively, assigning a new array value to an ORM element that
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replaces the old one will always trigger a change event.
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.. seealso::
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:class:`_types.ARRAY` - base array type
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:class:`_postgresql.array` - produces a literal array value.
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"""
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def __init__(
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self,
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item_type: _TypeEngineArgument[_T],
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as_tuple: bool = False,
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dimensions: Optional[int] = None,
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zero_indexes: bool = False,
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):
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"""Construct an ARRAY.
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E.g.::
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Column("myarray", ARRAY(Integer))
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Arguments are:
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:param item_type: The data type of items of this array. Note that
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dimensionality is irrelevant here, so multi-dimensional arrays like
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``INTEGER[][]``, are constructed as ``ARRAY(Integer)``, not as
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``ARRAY(ARRAY(Integer))`` or such.
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:param as_tuple=False: Specify whether return results
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should be converted to tuples from lists. DBAPIs such
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as psycopg2 return lists by default. When tuples are
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returned, the results are hashable.
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:param dimensions: if non-None, the ARRAY will assume a fixed
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number of dimensions. This will cause the DDL emitted for this
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ARRAY to include the exact number of bracket clauses ``[]``,
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and will also optimize the performance of the type overall.
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Note that PG arrays are always implicitly "non-dimensioned",
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meaning they can store any number of dimensions no matter how
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they were declared.
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:param zero_indexes=False: when True, index values will be converted
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between Python zero-based and PostgreSQL one-based indexes, e.g.
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a value of one will be added to all index values before passing
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to the database.
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"""
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if isinstance(item_type, ARRAY):
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raise ValueError(
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"Do not nest ARRAY types; ARRAY(basetype) "
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"handles multi-dimensional arrays of basetype"
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)
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if isinstance(item_type, type):
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item_type = item_type()
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self.item_type = item_type
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self.as_tuple = as_tuple
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self.dimensions = dimensions
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self.zero_indexes = zero_indexes
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class Comparator(sqltypes.ARRAY.Comparator[_CT]):
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"""Define comparison operations for :class:`_types.ARRAY`.
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Note that these operations are in addition to those provided
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by the base :class:`.types.ARRAY.Comparator` class, including
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:meth:`.types.ARRAY.Comparator.any` and
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:meth:`.types.ARRAY.Comparator.all`.
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"""
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def contains(
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self, other: typing_Any, **kwargs: typing_Any
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) -> ColumnElement[bool]:
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"""Boolean expression. Test if elements are a superset of the
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elements of the argument array expression.
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kwargs may be ignored by this operator but are required for API
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conformance.
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"""
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return self.operate(CONTAINS, other, result_type=sqltypes.Boolean)
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def contained_by(self, other: typing_Any) -> ColumnElement[bool]:
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"""Boolean expression. Test if elements are a proper subset of the
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elements of the argument array expression.
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"""
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return self.operate(
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CONTAINED_BY, other, result_type=sqltypes.Boolean
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)
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def overlap(self, other: typing_Any) -> ColumnElement[bool]:
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"""Boolean expression. Test if array has elements in common with
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an argument array expression.
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"""
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return self.operate(OVERLAP, other, result_type=sqltypes.Boolean)
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comparator_factory = Comparator
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@util.memoized_property
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def _against_native_enum(self) -> bool:
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return (
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isinstance(self.item_type, sqltypes.Enum)
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and self.item_type.native_enum
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)
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def literal_processor(
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self, dialect: Dialect
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) -> Optional[_LiteralProcessorType[_T]]:
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item_proc = self.item_type.dialect_impl(dialect).literal_processor(
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dialect
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)
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if item_proc is None:
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return None
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def to_str(elements: Iterable[typing_Any]) -> str:
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return f"ARRAY[{', '.join(elements)}]"
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def process(value: Sequence[typing_Any]) -> str:
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inner = self._apply_item_processor(
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value, item_proc, self.dimensions, to_str
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)
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return inner
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return process
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def bind_processor(
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self, dialect: Dialect
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) -> Optional[_BindProcessorType[Sequence[typing_Any]]]:
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item_proc = self.item_type.dialect_impl(dialect).bind_processor(
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dialect
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)
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def process(
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value: Optional[Sequence[typing_Any]],
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) -> Optional[list[typing_Any]]:
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if value is None:
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return value
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else:
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return self._apply_item_processor(
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value, item_proc, self.dimensions, list
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)
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return process
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def result_processor(
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self, dialect: Dialect, coltype: object
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) -> _ResultProcessorType[Sequence[typing_Any]]:
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item_proc = self.item_type.dialect_impl(dialect).result_processor(
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dialect, coltype
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)
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def process(
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value: Sequence[typing_Any],
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) -> Optional[Sequence[typing_Any]]:
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if value is None:
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return value
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else:
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return self._apply_item_processor(
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value,
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item_proc,
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self.dimensions,
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tuple if self.as_tuple else list,
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)
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if self._against_native_enum:
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super_rp = process
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pattern = re.compile(r"^{(.*)}$")
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def handle_raw_string(value: str) -> Sequence[Optional[str]]:
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inner = pattern.match(value).group(1) # type: ignore[union-attr] # noqa: E501
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return _split_enum_values(inner)
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def process(
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value: Sequence[typing_Any],
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|
) -> Optional[Sequence[typing_Any]]:
|
|
if value is None:
|
|
return value
|
|
# isinstance(value, str) is required to handle
|
|
# the case where a TypeDecorator for and Array of Enum is
|
|
# used like was required in sa < 1.3.17
|
|
return super_rp(
|
|
handle_raw_string(value)
|
|
if isinstance(value, str)
|
|
else value
|
|
)
|
|
|
|
return process
|
|
|
|
|
|
def _split_enum_values(array_string: str) -> Sequence[Optional[str]]:
|
|
if '"' not in array_string:
|
|
# no escape char is present so it can just split on the comma
|
|
return [
|
|
r if r != "NULL" else None
|
|
for r in (array_string.split(",") if array_string else [])
|
|
]
|
|
|
|
# handles quoted strings from:
|
|
# r'abc,"quoted","also\\\\quoted", "quoted, comma", "esc \" quot", qpr'
|
|
# returns
|
|
# ['abc', 'quoted', 'also\\quoted', 'quoted, comma', 'esc " quot', 'qpr']
|
|
text = array_string.replace(r"\"", "_$ESC_QUOTE$_")
|
|
text = text.replace(r"\\", "\\")
|
|
result = []
|
|
on_quotes = re.split(r'(")', text)
|
|
in_quotes = False
|
|
for tok in on_quotes:
|
|
if tok == '"':
|
|
in_quotes = not in_quotes
|
|
elif in_quotes:
|
|
result.append(tok.replace("_$ESC_QUOTE$_", '"'))
|
|
else:
|
|
# interpret NULL (without quotes!) as None
|
|
result.extend(
|
|
[
|
|
r if r != "NULL" else None
|
|
for r in re.findall(r"([^\s,]+),?", tok)
|
|
]
|
|
)
|
|
return result
|