ai_v/venv/Lib/site-packages/boto3/dynamodb/transform.py
24024 af7c11d7f9 feat(api): 实现图像生成及后台同步功能
- 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
2026-01-12 00:53:31 +08:00

344 lines
13 KiB
Python

# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# https://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file 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.
import copy
from boto3.compat import collections_abc
from boto3.docs.utils import DocumentModifiedShape
from boto3.dynamodb.conditions import ConditionBase, ConditionExpressionBuilder
from boto3.dynamodb.types import TypeDeserializer, TypeSerializer
def register_high_level_interface(base_classes, **kwargs):
base_classes.insert(0, DynamoDBHighLevelResource)
class _ForgetfulDict(dict):
"""A dictionary that discards any items set on it. For use as `memo` in
`copy.deepcopy()` when every instance of a repeated object in the deepcopied
data structure should result in a separate copy.
"""
def __setitem__(self, key, value):
pass
def copy_dynamodb_params(params, **kwargs):
return copy.deepcopy(params, memo=_ForgetfulDict())
class DynamoDBHighLevelResource:
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Apply handler that creates a copy of the user provided dynamodb
# item such that it can be modified.
self.meta.client.meta.events.register(
'provide-client-params.dynamodb',
copy_dynamodb_params,
unique_id='dynamodb-create-params-copy',
)
self._injector = TransformationInjector()
# Apply the handler that generates condition expressions including
# placeholders.
self.meta.client.meta.events.register(
'before-parameter-build.dynamodb',
self._injector.inject_condition_expressions,
unique_id='dynamodb-condition-expression',
)
# Apply the handler that serializes the request from python
# types to dynamodb types.
self.meta.client.meta.events.register(
'before-parameter-build.dynamodb',
self._injector.inject_attribute_value_input,
unique_id='dynamodb-attr-value-input',
)
# Apply the handler that deserializes the response from dynamodb
# types to python types.
self.meta.client.meta.events.register(
'after-call.dynamodb',
self._injector.inject_attribute_value_output,
unique_id='dynamodb-attr-value-output',
)
# Apply the documentation customizations to account for
# the transformations.
attr_value_shape_docs = DocumentModifiedShape(
'AttributeValue',
new_type='valid DynamoDB type',
new_description=(
'- The value of the attribute. The valid value types are '
'listed in the '
':ref:`DynamoDB Reference Guide<ref_valid_dynamodb_types>`.'
),
new_example_value=(
'\'string\'|123|Binary(b\'bytes\')|True|None|set([\'string\'])'
'|set([123])|set([Binary(b\'bytes\')])|[]|{}'
),
)
key_expression_shape_docs = DocumentModifiedShape(
'KeyExpression',
new_type=(
'condition from :py:class:`boto3.dynamodb.conditions.Key` '
'method'
),
new_description=(
'The condition(s) a key(s) must meet. Valid conditions are '
'listed in the '
':ref:`DynamoDB Reference Guide<ref_dynamodb_conditions>`.'
),
new_example_value='Key(\'mykey\').eq(\'myvalue\')',
)
con_expression_shape_docs = DocumentModifiedShape(
'ConditionExpression',
new_type=(
'condition from :py:class:`boto3.dynamodb.conditions.Attr` '
'method'
),
new_description=(
'The condition(s) an attribute(s) must meet. Valid conditions '
'are listed in the '
':ref:`DynamoDB Reference Guide<ref_dynamodb_conditions>`.'
),
new_example_value='Attr(\'myattribute\').eq(\'myvalue\')',
)
self.meta.client.meta.events.register(
'docs.*.dynamodb.*.complete-section',
attr_value_shape_docs.replace_documentation_for_matching_shape,
unique_id='dynamodb-attr-value-docs',
)
self.meta.client.meta.events.register(
'docs.*.dynamodb.*.complete-section',
key_expression_shape_docs.replace_documentation_for_matching_shape,
unique_id='dynamodb-key-expression-docs',
)
self.meta.client.meta.events.register(
'docs.*.dynamodb.*.complete-section',
con_expression_shape_docs.replace_documentation_for_matching_shape,
unique_id='dynamodb-cond-expression-docs',
)
class TransformationInjector:
"""Injects the transformations into the user provided parameters."""
def __init__(
self,
transformer=None,
condition_builder=None,
serializer=None,
deserializer=None,
):
self._transformer = transformer
if transformer is None:
self._transformer = ParameterTransformer()
self._condition_builder = condition_builder
if condition_builder is None:
self._condition_builder = ConditionExpressionBuilder()
self._serializer = serializer
if serializer is None:
self._serializer = TypeSerializer()
self._deserializer = deserializer
if deserializer is None:
self._deserializer = TypeDeserializer()
def inject_condition_expressions(self, params, model, **kwargs):
"""Injects the condition expression transformation into the parameters
This injection includes transformations for ConditionExpression shapes
and KeyExpression shapes. It also handles any placeholder names and
values that are generated when transforming the condition expressions.
"""
self._condition_builder.reset()
generated_names = {}
generated_values = {}
# Create and apply the Condition Expression transformation.
transformation = ConditionExpressionTransformation(
self._condition_builder,
placeholder_names=generated_names,
placeholder_values=generated_values,
is_key_condition=False,
)
self._transformer.transform(
params, model.input_shape, transformation, 'ConditionExpression'
)
# Create and apply the Key Condition Expression transformation.
transformation = ConditionExpressionTransformation(
self._condition_builder,
placeholder_names=generated_names,
placeholder_values=generated_values,
is_key_condition=True,
)
self._transformer.transform(
params, model.input_shape, transformation, 'KeyExpression'
)
expr_attr_names_input = 'ExpressionAttributeNames'
expr_attr_values_input = 'ExpressionAttributeValues'
# Now that all of the condition expression transformation are done,
# update the placeholder dictionaries in the request.
if expr_attr_names_input in params:
params[expr_attr_names_input].update(generated_names)
else:
if generated_names:
params[expr_attr_names_input] = generated_names
if expr_attr_values_input in params:
params[expr_attr_values_input].update(generated_values)
else:
if generated_values:
params[expr_attr_values_input] = generated_values
def inject_attribute_value_input(self, params, model, **kwargs):
"""Injects DynamoDB serialization into parameter input"""
self._transformer.transform(
params,
model.input_shape,
self._serializer.serialize,
'AttributeValue',
)
def inject_attribute_value_output(self, parsed, model, **kwargs):
"""Injects DynamoDB deserialization into responses"""
if model.output_shape is not None:
self._transformer.transform(
parsed,
model.output_shape,
self._deserializer.deserialize,
'AttributeValue',
)
class ConditionExpressionTransformation:
"""Provides a transformation for condition expressions
The ``ParameterTransformer`` class can call this class directly
to transform the condition expressions in the parameters provided.
"""
def __init__(
self,
condition_builder,
placeholder_names,
placeholder_values,
is_key_condition=False,
):
self._condition_builder = condition_builder
self._placeholder_names = placeholder_names
self._placeholder_values = placeholder_values
self._is_key_condition = is_key_condition
def __call__(self, value):
if isinstance(value, ConditionBase):
# Create a conditional expression string with placeholders
# for the provided condition.
built_expression = self._condition_builder.build_expression(
value, is_key_condition=self._is_key_condition
)
self._placeholder_names.update(
built_expression.attribute_name_placeholders
)
self._placeholder_values.update(
built_expression.attribute_value_placeholders
)
return built_expression.condition_expression
# Use the user provided value if it is not a ConditonBase object.
return value
class ParameterTransformer:
"""Transforms the input to and output from botocore based on shape"""
def transform(self, params, model, transformation, target_shape):
"""Transforms the dynamodb input to or output from botocore
It applies a specified transformation whenever a specific shape name
is encountered while traversing the parameters in the dictionary.
:param params: The parameters structure to transform.
:param model: The operation model.
:param transformation: The function to apply the parameter
:param target_shape: The name of the shape to apply the
transformation to
"""
self._transform_parameters(model, params, transformation, target_shape)
def _transform_parameters(
self, model, params, transformation, target_shape
):
type_name = model.type_name
if type_name in ('structure', 'map', 'list'):
getattr(self, f'_transform_{type_name}')(
model, params, transformation, target_shape
)
def _transform_structure(
self, model, params, transformation, target_shape
):
if not isinstance(params, collections_abc.Mapping):
return
for param in params:
if param in model.members:
member_model = model.members[param]
member_shape = member_model.name
if member_shape == target_shape:
params[param] = transformation(params[param])
else:
self._transform_parameters(
member_model,
params[param],
transformation,
target_shape,
)
def _transform_map(self, model, params, transformation, target_shape):
if not isinstance(params, collections_abc.Mapping):
return
value_model = model.value
value_shape = value_model.name
for key, value in params.items():
if value_shape == target_shape:
params[key] = transformation(value)
else:
self._transform_parameters(
value_model, params[key], transformation, target_shape
)
def _transform_list(self, model, params, transformation, target_shape):
if not isinstance(params, collections_abc.MutableSequence):
return
member_model = model.member
member_shape = member_model.name
for i, item in enumerate(params):
if member_shape == target_shape:
params[i] = transformation(item)
else:
self._transform_parameters(
member_model, params[i], transformation, target_shape
)