ai_v/venv/Lib/site-packages/boto3/resources/collection.py

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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
# Copyright 2014 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
import logging
from botocore import xform_name
from botocore.utils import merge_dicts
from ..docs import docstring
from .action import BatchAction
from .params import create_request_parameters
from .response import ResourceHandler
logger = logging.getLogger(__name__)
class ResourceCollection:
"""
Represents a collection of resources, which can be iterated through,
optionally with filtering. Collections automatically handle pagination
for you.
See :ref:`guide_collections` for a high-level overview of collections,
including when remote service requests are performed.
:type model: :py:class:`~boto3.resources.model.Collection`
:param model: Collection model
:type parent: :py:class:`~boto3.resources.base.ServiceResource`
:param parent: The collection's parent resource
:type handler: :py:class:`~boto3.resources.response.ResourceHandler`
:param handler: The resource response handler used to create resource
instances
"""
def __init__(self, model, parent, handler, **kwargs):
self._model = model
self._parent = parent
self._py_operation_name = xform_name(model.request.operation)
self._handler = handler
self._params = copy.deepcopy(kwargs)
def __repr__(self):
return '{}({}, {})'.format(
self.__class__.__name__,
self._parent,
f'{self._parent.meta.service_name}.{self._model.resource.type}',
)
def __iter__(self):
"""
A generator which yields resource instances after doing the
appropriate service operation calls and handling any pagination
on your behalf.
Page size, item limit, and filter parameters are applied
if they have previously been set.
>>> bucket = s3.Bucket('boto3')
>>> for obj in bucket.objects.all():
... print(obj.key)
'key1'
'key2'
"""
limit = self._params.get('limit', None)
count = 0
for page in self.pages():
for item in page:
yield item
# If the limit is set and has been reached, then
# we stop processing items here.
count += 1
if limit is not None and count >= limit:
return
def _clone(self, **kwargs):
"""
Create a clone of this collection. This is used by the methods
below to provide a chainable interface that returns copies
rather than the original. This allows things like:
>>> base = collection.filter(Param1=1)
>>> query1 = base.filter(Param2=2)
>>> query2 = base.filter(Param3=3)
>>> query1.params
{'Param1': 1, 'Param2': 2}
>>> query2.params
{'Param1': 1, 'Param3': 3}
:rtype: :py:class:`ResourceCollection`
:return: A clone of this resource collection
"""
params = copy.deepcopy(self._params)
merge_dicts(params, kwargs, append_lists=True)
clone = self.__class__(
self._model, self._parent, self._handler, **params
)
return clone
def pages(self):
"""
A generator which yields pages of resource instances after
doing the appropriate service operation calls and handling
any pagination on your behalf. Non-paginated calls will
return a single page of items.
Page size, item limit, and filter parameters are applied
if they have previously been set.
>>> bucket = s3.Bucket('boto3')
>>> for page in bucket.objects.pages():
... for obj in page:
... print(obj.key)
'key1'
'key2'
:rtype: list(:py:class:`~boto3.resources.base.ServiceResource`)
:return: List of resource instances
"""
client = self._parent.meta.client
cleaned_params = self._params.copy()
limit = cleaned_params.pop('limit', None)
page_size = cleaned_params.pop('page_size', None)
params = create_request_parameters(self._parent, self._model.request)
merge_dicts(params, cleaned_params, append_lists=True)
# Is this a paginated operation? If so, we need to get an
# iterator for the various pages. If not, then we simply
# call the operation and return the result as a single
# page in a list. For non-paginated results, we just ignore
# the page size parameter.
if client.can_paginate(self._py_operation_name):
logger.debug(
'Calling paginated %s:%s with %r',
self._parent.meta.service_name,
self._py_operation_name,
params,
)
paginator = client.get_paginator(self._py_operation_name)
pages = paginator.paginate(
PaginationConfig={'MaxItems': limit, 'PageSize': page_size},
**params,
)
else:
logger.debug(
'Calling %s:%s with %r',
self._parent.meta.service_name,
self._py_operation_name,
params,
)
pages = [getattr(client, self._py_operation_name)(**params)]
# Now that we have a page iterator or single page of results
# we start processing and yielding individual items.
count = 0
for page in pages:
page_items = []
for item in self._handler(self._parent, params, page):
page_items.append(item)
# If the limit is set and has been reached, then
# we stop processing items here.
count += 1
if limit is not None and count >= limit:
break
yield page_items
# Stop reading pages if we've reached out limit
if limit is not None and count >= limit:
break
def all(self):
"""
Get all items from the collection, optionally with a custom
page size and item count limit.
This method returns an iterable generator which yields
individual resource instances. Example use::
# Iterate through items
>>> for queue in sqs.queues.all():
... print(queue.url)
'https://url1'
'https://url2'
# Convert to list
>>> queues = list(sqs.queues.all())
>>> len(queues)
2
"""
return self._clone()
def filter(self, **kwargs):
"""
Get items from the collection, passing keyword arguments along
as parameters to the underlying service operation, which are
typically used to filter the results.
This method returns an iterable generator which yields
individual resource instances. Example use::
# Iterate through items
>>> for queue in sqs.queues.filter(Param='foo'):
... print(queue.url)
'https://url1'
'https://url2'
# Convert to list
>>> queues = list(sqs.queues.filter(Param='foo'))
>>> len(queues)
2
:rtype: :py:class:`ResourceCollection`
"""
return self._clone(**kwargs)
def limit(self, count):
"""
Return at most this many resources.
>>> for bucket in s3.buckets.limit(5):
... print(bucket.name)
'bucket1'
'bucket2'
'bucket3'
'bucket4'
'bucket5'
:type count: int
:param count: Return no more than this many items
:rtype: :py:class:`ResourceCollection`
"""
return self._clone(limit=count)
def page_size(self, count):
"""
Fetch at most this many resources per service request.
>>> for obj in s3.Bucket('boto3').objects.page_size(100):
... print(obj.key)
:type count: int
:param count: Fetch this many items per request
:rtype: :py:class:`ResourceCollection`
"""
return self._clone(page_size=count)
class CollectionManager:
"""
A collection manager provides access to resource collection instances,
which can be iterated and filtered. The manager exposes some
convenience functions that are also found on resource collections,
such as :py:meth:`~ResourceCollection.all` and
:py:meth:`~ResourceCollection.filter`.
Get all items::
>>> for bucket in s3.buckets.all():
... print(bucket.name)
Get only some items via filtering::
>>> for queue in sqs.queues.filter(QueueNamePrefix='AWS'):
... print(queue.url)
Get whole pages of items:
>>> for page in s3.Bucket('boto3').objects.pages():
... for obj in page:
... print(obj.key)
A collection manager is not iterable. You **must** call one of the
methods that return a :py:class:`ResourceCollection` before trying
to iterate, slice, or convert to a list.
See the :ref:`guide_collections` guide for a high-level overview
of collections, including when remote service requests are performed.
:type collection_model: :py:class:`~boto3.resources.model.Collection`
:param model: Collection model
:type parent: :py:class:`~boto3.resources.base.ServiceResource`
:param parent: The collection's parent resource
:type factory: :py:class:`~boto3.resources.factory.ResourceFactory`
:param factory: The resource factory to create new resources
:type service_context: :py:class:`~boto3.utils.ServiceContext`
:param service_context: Context about the AWS service
"""
# The class to use when creating an iterator
_collection_cls = ResourceCollection
def __init__(self, collection_model, parent, factory, service_context):
self._model = collection_model
operation_name = self._model.request.operation
self._parent = parent
search_path = collection_model.resource.path
self._handler = ResourceHandler(
search_path=search_path,
factory=factory,
resource_model=collection_model.resource,
service_context=service_context,
operation_name=operation_name,
)
def __repr__(self):
return '{}({}, {})'.format(
self.__class__.__name__,
self._parent,
f'{self._parent.meta.service_name}.{self._model.resource.type}',
)
def iterator(self, **kwargs):
"""
Get a resource collection iterator from this manager.
:rtype: :py:class:`ResourceCollection`
:return: An iterable representing the collection of resources
"""
return self._collection_cls(
self._model, self._parent, self._handler, **kwargs
)
# Set up some methods to proxy ResourceCollection methods
def all(self):
return self.iterator()
all.__doc__ = ResourceCollection.all.__doc__
def filter(self, **kwargs):
return self.iterator(**kwargs)
filter.__doc__ = ResourceCollection.filter.__doc__
def limit(self, count):
return self.iterator(limit=count)
limit.__doc__ = ResourceCollection.limit.__doc__
def page_size(self, count):
return self.iterator(page_size=count)
page_size.__doc__ = ResourceCollection.page_size.__doc__
def pages(self):
return self.iterator().pages()
pages.__doc__ = ResourceCollection.pages.__doc__
class CollectionFactory:
"""
A factory to create new
:py:class:`CollectionManager` and :py:class:`ResourceCollection`
subclasses from a :py:class:`~boto3.resources.model.Collection`
model. These subclasses include methods to perform batch operations.
"""
def load_from_definition(
self, resource_name, collection_model, service_context, event_emitter
):
"""
Loads a collection from a model, creating a new
:py:class:`CollectionManager` subclass
with the correct properties and methods, named based on the service
and resource name, e.g. ec2.InstanceCollectionManager. It also
creates a new :py:class:`ResourceCollection` subclass which is used
by the new manager class.
:type resource_name: string
:param resource_name: Name of the resource to look up. For services,
this should match the ``service_name``.
:type service_context: :py:class:`~boto3.utils.ServiceContext`
:param service_context: Context about the AWS service
:type event_emitter: :py:class:`~botocore.hooks.HierarchialEmitter`
:param event_emitter: An event emitter
:rtype: Subclass of :py:class:`CollectionManager`
:return: The collection class.
"""
attrs = {}
collection_name = collection_model.name
# Create the batch actions for a collection
self._load_batch_actions(
attrs,
resource_name,
collection_model,
service_context.service_model,
event_emitter,
)
# Add the documentation to the collection class's methods
self._load_documented_collection_methods(
attrs=attrs,
resource_name=resource_name,
collection_model=collection_model,
service_model=service_context.service_model,
event_emitter=event_emitter,
base_class=ResourceCollection,
)
if service_context.service_name == resource_name:
cls_name = (
f'{service_context.service_name}.{collection_name}Collection'
)
else:
cls_name = f'{service_context.service_name}.{resource_name}.{collection_name}Collection'
collection_cls = type(str(cls_name), (ResourceCollection,), attrs)
# Add the documentation to the collection manager's methods
self._load_documented_collection_methods(
attrs=attrs,
resource_name=resource_name,
collection_model=collection_model,
service_model=service_context.service_model,
event_emitter=event_emitter,
base_class=CollectionManager,
)
attrs['_collection_cls'] = collection_cls
cls_name += 'Manager'
return type(str(cls_name), (CollectionManager,), attrs)
def _load_batch_actions(
self,
attrs,
resource_name,
collection_model,
service_model,
event_emitter,
):
"""
Batch actions on the collection become methods on both
the collection manager and iterators.
"""
for action_model in collection_model.batch_actions:
snake_cased = xform_name(action_model.name)
attrs[snake_cased] = self._create_batch_action(
resource_name,
snake_cased,
action_model,
collection_model,
service_model,
event_emitter,
)
def _load_documented_collection_methods(
factory_self,
attrs,
resource_name,
collection_model,
service_model,
event_emitter,
base_class,
):
# The base class already has these methods defined. However
# the docstrings are generic and not based for a particular service
# or resource. So we override these methods by proxying to the
# base class's builtin method and adding a docstring
# that pertains to the resource.
# A collection's all() method.
def all(self):
return base_class.all(self)
all.__doc__ = docstring.CollectionMethodDocstring(
resource_name=resource_name,
action_name='all',
event_emitter=event_emitter,
collection_model=collection_model,
service_model=service_model,
include_signature=False,
)
attrs['all'] = all
# The collection's filter() method.
def filter(self, **kwargs):
return base_class.filter(self, **kwargs)
filter.__doc__ = docstring.CollectionMethodDocstring(
resource_name=resource_name,
action_name='filter',
event_emitter=event_emitter,
collection_model=collection_model,
service_model=service_model,
include_signature=False,
)
attrs['filter'] = filter
# The collection's limit method.
def limit(self, count):
return base_class.limit(self, count)
limit.__doc__ = docstring.CollectionMethodDocstring(
resource_name=resource_name,
action_name='limit',
event_emitter=event_emitter,
collection_model=collection_model,
service_model=service_model,
include_signature=False,
)
attrs['limit'] = limit
# The collection's page_size method.
def page_size(self, count):
return base_class.page_size(self, count)
page_size.__doc__ = docstring.CollectionMethodDocstring(
resource_name=resource_name,
action_name='page_size',
event_emitter=event_emitter,
collection_model=collection_model,
service_model=service_model,
include_signature=False,
)
attrs['page_size'] = page_size
def _create_batch_action(
factory_self,
resource_name,
snake_cased,
action_model,
collection_model,
service_model,
event_emitter,
):
"""
Creates a new method which makes a batch operation request
to the underlying service API.
"""
action = BatchAction(action_model)
def batch_action(self, *args, **kwargs):
return action(self, *args, **kwargs)
batch_action.__name__ = str(snake_cased)
batch_action.__doc__ = docstring.BatchActionDocstring(
resource_name=resource_name,
event_emitter=event_emitter,
batch_action_model=action_model,
service_model=service_model,
collection_model=collection_model,
include_signature=False,
)
return batch_action