# packages

pkgId: 0c9d9ab7563893ac4fd2b99356f10900a0f737acd3b18554b7abbc832172fcb6
name: python-yoyo-migrations
arch: src
version: 8.2.0
epoch: 0
release: 1
summary: Database migrations with SQL
url: https://ollycope.com/software/yoyo/latest/
rpm_license: Apache
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-6acea73b-4400-4723-9f33-87f0910fd6ef
location_href: 00011958-python-yoyo-migrations/python-yoyo-migrations-8.2.0-1.src.rpm
checksum_type: sha256
description:
  Yoyo-migrations is a database schema migration tool.
  Database migrations can be written as SQL files or Python scripts.


pkgId: 3ff9a08cb31cf4401a780fd795c2166d7f2d9db6851dee4a728530865076f5fb
name: python-yui
arch: src
version: 1.0.8
epoch: 0
release: 1
summary: Yui: git based personal task manager
url: https://yuistaskmanager.github.io/
rpm_license: MIT License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-b0303c28-68b6-4281-aefe-03629256e924
location_href: 00005531-python-yui/python-yui-1.0.8-1.src.rpm
checksum_type: sha256
description:
   7  | personal | yui        | show creation date column in task list                                 | new
   15 | personal | yui        | sanitize slashes in task filename                                      | new
  ```
  First column is *taskId* you will need it to manipulate the task

  Pick all tasks you are planning to work with today.
  ```
  yui pick %taskId%
  ```
  Will pick the task
  ```
  yui list cur
  ```
  Will display tasks for current day.
  If you made a mistake, you can return the task back to heap
  ```
  yui reset %taskId%
  ```

  ```
  yui open %taskId%
  ```

  Adjust yaml header, simple replace `status: new` with `status: work`.
  You can use any custom statuses, but buildin - *new*, *work*, *done* will be highlighted in `yui list` output.

  As for it's just .md file, you can make any notes behind yaml header.

  That's just plain text files, after all.

  In yaml header, replace `status: work` with `status: done`.

  ```
  yui list cur
  ```
  Will get more green lines while you complete the tasks.

  ```
  yui reset all
  ```
  Will return unfinished tasks back to the heap
  ```
  yui archive today
  ```
  Will move only *done* tasks to archive folder with current day. You can also use "yesterday" or specify date as "YYYY-MM-DD", or anything else recognized by php `strtotime()` function.
  To view archived tasks use:
  ```
  yui list YYYY-MM-DD
  ```

  All git commands are mapped with `yui git`.
  Most used:
      - `yui git log` history of changes in tasks
      - `yui git remote add origin %link%` link your task list with remote repository
      - `yui git push` save local changes to remote repository
      - `yui git pull` load fresh changes from remote repository

  Defaut:
   - Linux: `~/.yui`
   - Windows: `c:\Users\%username%\AppData\Local\yui`
  Can be overwriten with `YUI_HOME` environment variable, like `YUI_HOME=mypath yui list heap`

  > Location: %application-working-folder%/config.yaml
  Example:


pkgId: d676722bbd7762f30aeada7f7a1540d4eee61da75499707537e6db20c709aece
name: python-zabbix-api
arch: src
version: 0.5.5
epoch: 0
release: 1
summary: Zabbix API
url: https://github.com/gescheit/scripts
rpm_license: GNU LGPL 2.1
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-6ea9488d-765f-411b-b66e-867048d0b6f7
location_href: 00013746-python-zabbix-api/python-zabbix-api-0.5.5-1.src.rpm
checksum_type: sha256
description:
  This is an implementation of the Zabbix API in Python.
  Please note that the Zabbix API is still in a draft state,
  and subject to change.

  Implementations of the Zabbix API in other languages may
  be found on the wiki.

  Zabbix 1.8, 2.0, 2.2, 2.4, 3.0 and 3.2 are supported.
  Python 2 and 3 are supported.

  Future versions must be supported too, if there is no deep changes.

  Installation:
  ```sh

  ```

  Short example:

  ```python
  >>> from zabbix_api import ZabbixAPI
  >>> zapi = ZabbixAPI(server="https://server/")
  >>> zapi.login("login", "password")
  >>> zapi.trigger.get({"expandExpression": "extend", "triggerids": range(0, 100)})
  ```

  See also:
  * http://www.zabbix.com/wiki/doc/api
  * https://www.zabbix.com/documentation/2.4/manual/api
  * http://www.zabbix.com/forum/showthread.php?t=15218


pkgId: 018c7e9207a88c290b3d387f84821c23e2711180b6441bc2e1e68c7b2c406318
name: python-zake
arch: src
version: 0.2.2
epoch: 0
release: 1
summary: A python package that works to provide a nice set of testing utilities for the kazoo library.
url: https://github.com/yahoo/Zake
rpm_license: ASL 2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-c67c3d8b-4685-49bc-ab0f-cdc3334e0e0f
location_href: 00005534-python-zake/python-zake-0.2.2-1.src.rpm
checksum_type: sha256
description:
  A python package that works to provide a nice set of testing utilities for the `kazoo`_ library.
  It includes the following functionality:
  * Storage access (for viewing what was saved/created).
  * Kazoo *mostly* compatible client API.
  * Sync/transaction/create/get/delete... commands.
  * Listener support.
  * And more...
  It simplifies testing by providing a client that has a similar API as the kazoo
  client so that your tests (or applications/libraries that use kazoo) do not
  require a real zookeeper server to be  tested with (since this is not available
  in all testing environments).


pkgId: 6b4f73bf98b947da59a14b8e8f08dc09d3b58c15c8f5d0c776e3aee83de99dc5
name: python-zanata2fedmsg
arch: src
version: 0.2
epoch: 0
release: 1
summary: zanata2fedmsg bridges zanata to fedmsg
url: https://github.com/fedora-infra/zanata2fedmsg
rpm_license: GPLv3+
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-b81e0b47-d7c7-4262-8e69-ab0fe1f490a1
location_href: 00005536-python-zanata2fedmsg/python-zanata2fedmsg-0.2-1.src.rpm
checksum_type: sha256
description:
  Run the server::
      $ ZANATA2FEDMSG_CONFIG=./dev.ini python zanata2fedmsg.py
  Generate example urls for zanata::
      $ ZANATA2FEDMSG_CONFIG=$(pwd)/dev.ini python utility/zanata2fedmsg-webhook-generator.py asknot-ng
      URL for asknot-ng is
      https://apps.fedoraproject.org/zanata2fedmsg/webhook/5c605f0f4dd27a5cd81b053bedd06ec153f2272f82c8a49b0842eab7f3c73141


pkgId: 017c5e452d03af3eade29d5acb6b39f0d5e151c354e038d915a6c8a32bb17681
name: python-zappa
arch: src
version: 0.56.1
epoch: 0
release: 1
summary: Server-less Python Web Services for AWS Lambda and API Gateway
url: https://github.com/zappa/Zappa
rpm_license: MIT License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-ee955bd4-f461-41cd-bd8e-4ea20fe96107
location_href: 00011445-python-zappa/python-zappa-0.56.1-1.src.rpm
checksum_type: sha256
description:
  dev:
    app_function: your_module.your_app
    s3_bucket: your-code-bucket
    events:
    - function: your_module.your_function
      event_source:
        arn: arn:aws:s3:::your-event-bucket
        events:
        - s3:ObjectCreated:*
  ```
  You can also supply a custom settings file at any time with the `-s` argument, ex:
  ```
  $ zappa deploy dev -s my-custom-settings.yml
  ```
  Similarly, you can supply a `zappa_settings.toml` file:
  ```toml
  [dev]
    app_function = "your_module.your_app"
    s3_bucket = "your-code-bucket"
  ```


  Zappa will automatically set up a regularly occurring execution of your application in order to keep the Lambda function warm. This can be disabled via the `keep_warm` setting.

  Zappa is now able to serve and receive binary files, as detected by their MIME-type.
  However, generally Zappa is designed for running your application code, not for serving static web assets. If you plan on serving custom static assets in your web application (CSS/JavaScript/images/etc.,), you'll likely want to use a combination of AWS S3 and AWS CloudFront.
  Your web application framework will likely be able to handle this for you automatically. For Flask, there is [Flask-S3](https://github.com/e-dard/flask-s3), and for Django, there is [Django-Storages](https://django-storages.readthedocs.io/en/latest/).
  Similarly, you may want to design your application so that static binary uploads go [directly to S3](http://docs.aws.amazon.com/AWSJavaScriptSDK/guide/browser-examples.html#Uploading_a_local_file_using_the_File_API), which then triggers an event response defined in your `events` setting! That's thinking serverlessly!

  The simplest way to enable CORS (Cross-Origin Resource Sharing) for your Zappa application is to set `cors` to `true` in your Zappa settings file and update, which is the equivalent of pushing the "Enable CORS" button in the AWS API Gateway console. This is disabled by default, but you may wish to enable it for APIs which are accessed from other domains, etc.
  You can also simply handle CORS directly in your application. Your web framework will probably have an extension to do this, such as [django-cors-headers](https://github.com/ottoyiu/django-cors-headers) or [Flask-CORS](https://github.com/corydolphin/flask-cors). Using these will make your code more portable.

  AWS currently limits Lambda zip sizes to 50 megabytes. If your project is larger than that, set `slim_handler: true` in your `zappa_settings.json`. In this case, your fat application package will be replaced with a small handler-only package. The handler file then pulls the rest of the large project down from S3 at run time! The initial load of the large project may add to startup overhead, but the difference should be minimal on a warm lambda function. Note that this will also eat into the storage space of your application function. Note that AWS currently [limits](https://docs.aws.amazon.com/lambda/latest/dg/limits.html) the `/tmp` directory storage to 512 MB, so your project must still be smaller than that.

  Bash completion can be enabled by adding the following to your .bashrc:
  ```bash
    eval "$(register-python-argcomplete zappa)"
  ```
  `register-python-argcomplete` is provided by the argcomplete Python package. If this package was installed in a virtualenv
  then the command must be run there. Alternatively you can execute:
    activate-global-python-argcomplete --dest=- > file
  The file's contents should then be sourced in e.g. ~/.bashrc.


  You can use the `api_key_required` setting to generate an API key to all the routes of your API Gateway. The process is as follows:
  1. Deploy/redeploy (update won't work) and write down the *id* for the key that has been created
  2. Go to AWS console > Amazon API Gateway and
      * select "API Keys" and find the key *value* (for example `key_value`)
      * select "Usage Plans", create a new usage plan and link the API Key and the API that Zappa has created for you
  3. Send a request where you pass the key value as a header called `x-api-key` to access the restricted endpoints (for example with curl: `curl --header "x-api-key: key_value"`). Note that without the x-api-key header, you will receive a 403.

  You can enable IAM-based (v4 signing) authorization on an API by setting the `iam_authorization` setting to `true`. Your API will then require signed requests and access can be controlled via [IAM policy](https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-iam-policy-examples.html). Unsigned requests will receive a 403 response, as will requesters who are not authorized to access the API. Enabling this will override the Authorizer configuration (see below).

  If you deploy an API endpoint with Zappa, you can take advantage of [API Gateway Lambda Authorizers](https://docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-use-lambda-authorizer.html) to implement a token-based authentication - all you need to do is to provide a function to create the required output, Zappa takes care of the rest. A good start for the function is the [AWS Labs blueprint example](https://github.com/awslabs/aws-apigateway-lambda-authorizer-blueprints/blob/master/blueprints/python/api-gateway-authorizer-python.py).
  If you are wondering for what you would use an Authorizer, here are some potential use cases:
  1. Call out to OAuth provider
  2. Decode a JWT token inline
  3. Lookup in a self-managed DB (for example DynamoDB)
  Zappa can be configured to call a function inside your code to do the authorization, or to call some other existing lambda function (which lets you share the authorizer between multiple lambdas). You control the behavior by specifying either the `arn` or `function_name` values in the `authorizer` settings block.
  For example, to get the Cognito identity, add this to a `zappa_settings.yaml`:
  ```yaml
    context_header_mappings:
      user_id: authorizer.user_id
  ```
  Which can now be accessed in Flask like this:
  ```python
  from flask import request
  @route('/hello')
  def hello_world:
     print(request.headers.get('user_id'))
  ```

  You can also use AWS Cognito User Pool Authorizer by adding:
  ```javascript
  {
      "authorizer": {
          "type": "COGNITO_USER_POOLS",
          "provider_arns": [
              "arn:aws:cognito-idp:{region}:{account_id}:userpool/{user_pool_id}"
          ]
      }
  }
  ```

  You can also use API Gateway Resource Policies. Example of IP Whitelisting:
  ```javascript
  {
      "Version": "2012-10-17",
      "Statement": [
          {
              "Effect": "Allow",
              "Principal": "*",
              "Action": "execute-api:Invoke",
              "Resource": "execute-api:/*",
              "Condition": {
                  "IpAddress": {
                      "aws:SourceIp": [
                          "1.2.3.4/32"
                      ]
                  }
              }
          }
      ]
  }
  ```


  If you want to set local environment variables for a deployment stage, you can simply set them in your `zappa_settings.json`:
  ```javascript
  {
      "dev": {
          "environment_variables": {
              "your_key": "your_value"
          }
      },
  }
  ```
  You can then access these inside your application with:
  ```python
  import os
  your_value = os.environ.get('your_key')
  ```
  If your project needs to be aware of the type of environment you're deployed to, you'll also be able to get `SERVERTYPE` (AWS Lambda), `FRAMEWORK` (Zappa), `PROJECT` (your project name) and `STAGE` (_dev_, _production_, etc.) variables at any time.

  If you want to use native AWS Lambda environment variables you can use the `aws_environment_variables` configuration setting. These are useful as you can easily change them via the AWS Lambda console or cli at runtime. They are also useful for storing sensitive credentials and to take advantage of KMS encryption of environment variables.
  During development, you can add your Zappa defined variables to your locally running app by, for example, using the below (for Django, to manage.py).
  ```python
  if 'SERVERTYPE' in os.environ and os.environ['SERVERTYPE'] == 'AWS Lambda':
      import json
      import os
      json_data = open('zappa_settings.json')
      env_vars = json.load(json_data)['dev']['environment_variables']
      for key, val in env_vars.items():
          os.environ[key] = val
  ```

  Any environment variables that you have set outside of Zappa (via AWS Lambda console or cli) will remain as they are when running `update`, unless they are also in `aws_environment_variables`, in which case the remote value will be overwritten by the one in the settings file. If you are using KMS-encrypted AWS environment variables, you can set your KMS Key ARN in the `aws_kms_key_arn` setting. Make sure that the values you set are encrypted in such case.
  _Note: if you rely on these as well as `environment_variables`, and you have the same key names, then those in `environment_variables` will take precedence as they are injected in the lambda handler._

  _S3 remote environment variables were added to Zappa before AWS introduced native environment variables for Lambda (via the console and cli). Before going down this route check if above make more sense for your usecase._
  If you want to use remote environment variables to configure your application (which is especially useful for things like sensitive credentials), you can create a file and place it in an S3 bucket to which your Zappa application has access. To do this, add the `remote_env` key to zappa_settings pointing to a file containing a flat JSON object, so that each key-value pair on the object will be set as an environment variable and value whenever a new lambda instance spins up.
  For example, to ensure your application has access to the database credentials without storing them in your version control, you can add a file to S3 with the connection string and load it into the lambda environment using the `remote_env` configuration setting.
  super-secret-config.json (uploaded to my-config-bucket):
  ```javascript
  {
      "DB_CONNECTION_STRING": "super-secret:database"
  }
  ```
  zappa_settings.json:
  ```javascript
  {
      "dev": {
          "remote_env": "s3://my-config-bucket/super-secret-config.json",
      },
  }
  ```
  Now in your application you can use:
  ```python
  import os
  db_string = os.environ.get('DB_CONNECTION_STRING')
  ```

  If you want to map an API Gateway context variable (http://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-mapping-template-reference.html) to an HTTP header you can set up the mapping in `zappa_settings.json`:
  ```javascript
  {
      "dev": {
          "context_header_mappings": {
              "HTTP_header_name": "API_Gateway_context_variable"
          }
      },
  }
  ```
  For example, if you want to expose the $context.identity.cognitoIdentityId variable as the HTTP header CognitoIdentityId, and $context.stage as APIStage, you would have:
  ```javascript
  {
      "dev": {
          "context_header_mappings": {
              "CognitoIdentityId": "identity.cognitoIdentityId",
              "APIStage": "stage"
          }
      },
  }
  ```

  By default, if an _unhandled_ exception happens in your code, Zappa will just print the stacktrace into a CloudWatch log. If you wish to use an external reporting tool to take note of those exceptions, you can use the `exception_handler` configuration option.
  zappa_settings.json:
  ```javascript
  {
      "dev": {
          "exception_handler": "your_module.unhandled_exceptions",
      },
  }
  ```
  The function has to accept three arguments: exception, event, and context:
  your_module.py
  ```python
  def unhandled_exceptions(e, event, context):
      send_to_raygun(e, event)  # gather data you need and send
      return True # Prevent invocation retry
  ```
  You may still need a similar exception handler inside your application, this is just a way to catch exception which happen at the Zappa/WSGI layer (typically event-based invocations, misconfigured settings, bad Lambda packages, and permissions issues).
  By default, AWS Lambda will attempt to retry an event based (non-API Gateway, e.g. CloudWatch) invocation if an exception has been thrown. However, you can prevent this by returning True, as in example above, so Zappa that will not re-raise the uncaught exception, thus preventing AWS Lambda from retrying the current invocation.


  You can specify which _local_ profile to use for deploying your Zappa application by defining
  the `profile_name` setting, which will correspond to a profile in your AWS credentials file.

  The default IAM policy created by Zappa for executing the Lambda is very permissive.
  It grants access to all actions for
  all resources for types CloudWatch, S3, Kinesis, SNS, SQS, DynamoDB, and Route53; lambda:InvokeFunction
  for all Lambda resources; Put to all X-Ray resources; and all Network Interface operations to all EC2
  resources. While this allows most Lambdas to work correctly with no extra permissions, it is
  generally not an acceptable set of permissions for most continuous integration pipelines or
  production deployments. Instead, you will probably want to manually manage your IAM policies.
  To manually define the policy of your Lambda execution role, you must set *manage_roles* to false and define
  either the *role_name* or *role_arn* in your Zappa settings file.
  ```javascript
  {
      "dev": {
          "manage_roles": false, // Disable Zappa client managing roles.
          "role_name": "MyLambdaRole", // Name of your Zappa execution role. Optional, default: <project_name>-<env>-ZappaExecutionRole.
          "role_arn": "arn:aws:iam::12345:role/app-ZappaLambdaExecutionRole", // ARN of your Zappa execution role. Optional.
      },
  }
  ```
  Ongoing discussion about the minimum policy requirements necessary for a Zappa deployment [can be found here](https://github.com/Miserlou/Zappa/issues/244).
  A more robust solution to managing these entitlements will likely be implemented soon.
  To add permissions to the default Zappa execution policy, use the `extra_permissions` setting:
  ```javascript
  {
      "dev": {
          "extra_permissions": [{ // Attach any extra permissions to this policy.
              "Effect": "Allow",
              "Action": ["rekognition:*"], // AWS Service ARN
              "Resource": "*"
          }]
      },
  }
  ```

  Zappa can enable [AWS X-Ray](https://aws.amazon.com/xray/) support on your function with a configuration setting:
  ```javascript
  {
      "dev": {
          "xray_tracing": true
      },
  }
  ```
  This will enable it on the Lambda function and allow you to instrument your code with X-Ray.
  For example, with Flask:
  ```python
  from aws_xray_sdk.core import xray_recorder
  app = Flask(__name__)
  xray_recorder.configure(service='my_app_name')
  @route('/hello')
  @xray_recorder.capture('hello')
  def hello_world:
      return 'Hello'
  ```
  You may use the capture decorator to create subsegments around functions, or `xray_recorder.begin_subsegment('subsegment_name')` and `xray_recorder.end_subsegment()` within a function. The official [X-Ray documentation for Python](http://docs.aws.amazon.com/xray-sdk-for-python/latest/reference/) has more information on how to use this with your code.
  Note that you may create subsegments in your code but an exception will be raised if you try to create a segment, as it is [created by the lambda worker](https://github.com/aws/aws-xray-sdk-python/issues/2). This also means that if you use Flask you must not use the [XRayMiddleware the documentation suggests](https://docs.aws.amazon.com/xray/latest/devguide/xray-sdk-python-middleware.html).

  <p align="center">
    <a href="https://htmlpreview.github.io/?https://github.com/Miserlou/Talks/blob/master/serverless-london/global.html#0"><img src="http://i.imgur.com/oR61Qau.png" alt="Global Zappa Slides"/></a>
  </p>
  <p align="center">
    <i>Click to see <a href="https://htmlpreview.github.io/?https://github.com/Miserlou/Talks/blob/master/serverless-london/global.html#0">slides from ServerlessConf London</a>!</i>
  </p>
  During the `init` process, you will be given the option to deploy your application "globally." This will allow you to deploy your application to all available AWS regions simultaneously in order to provide a consistent global speed, increased redundancy, data isolation, and legal compliance. You can also choose to deploy only to "primary" locations, the AWS regions with `-1` in their names.
  To learn more about these capabilities, see [these slides](https://htmlpreview.github.io/?https://github.com/Miserlou/Talks/blob/master/serverless-london/global.html#0) from ServerlessConf London.

  Out of the box, AWS sets a limit of [1000 concurrent executions](http://docs.aws.amazon.com/lambda/latest/dg/limits.html) for your functions. If you start to breach these limits, you may start to see errors like `ClientError: An error occurred (LimitExceededException) when calling the PutTargets.."` or something similar.
  To avoid this, you can file a [service ticket](https://console.aws.amazon.com/support/home#/) with Amazon to raise your limits up to the many tens of thousands of concurrent executions which you may need. This is a fairly common practice with Amazon, designed to prevent you from accidentally creating extremely expensive bug reports. So, before raising your service limits, make sure that you don't have any rogue scripts which could accidentally create tens of thousands of parallel executions that you don't want to pay for.

  If you want to utilise [AWS Lambda's Dead Letter Queue feature](http://docs.aws.amazon.com/lambda/latest/dg/dlq.html) simply add the key `dead_letter_arn`, with the value being the complete ARN to the corresponding SNS topic or SQS queue in your `zappa_settings.json`.
  You must have already created the corresponding SNS/SQS topic/queue, and the Lambda function execution role must have been provisioned with read/publish/sendMessage access to the DLQ resource.

  For monitoring of different deployments, a unique UUID for each package is available in `package_info.json` in the root directory of your application's package.  You can use this information or a hash of this file for such things as tracking errors across different deployments, monitoring status of deployments and other such things on services such as Sentry and New Relic. The package will contain:
  ```json
  {
    "build_platform": "darwin",
    "build_user": "frank",
    "build_time": "1509732511",
    "uuid": "9c2df9e6-30f4-4c0a-ac4d-4ecb51831a74"
  }
  ```

  Zappa can be used to handle events triggered by Application Load Balancers (ALB). This can be useful in a few circumstances:
  - Since API Gateway has a hard limit of 30 seconds before timing out, you can use an ALB for longer running requests.
  - API Gateway is billed per-request; therefore, costs can become excessive with high throughput services. ALBs pricing model makes much more sense financially if you're expecting a lot of traffic to your Lambda.
  - ALBs can be placed within a VPC, which may make more sense for private endpoints than using API Gateway's private model (using AWS PrivateLink).
  Like API Gateway, Zappa can automatically provision ALB resources for you.  You'll need to add the following to your `zappa_settings`:
  ```
  "alb_enabled": true,
  "alb_vpc_config": {
      "CertificateArn": "arn:aws:acm:us-east-1:[your-account-id]:certificate/[certificate-id]",
      "SubnetIds": [
          // Here, you'll want to provide a list of subnets for your ALB, eg. 'subnet-02a58266'
      ],
      "SecurityGroupIds": [
          // And here, a list of security group IDs, eg. 'sg-fbacb791'
      ]
  }
  ```
  More information on using ALB as an event source for Lambda can be found [here](https://docs.aws.amazon.com/elasticloadbalancing/latest/application/lambda-functions.html).
  *An important note*: right now, Zappa will provision ONE lambda to ONE load balancer, which means using `base_path` along with ALB configuration is currently unsupported.

  API Gateway can be configured to be only accessible in a VPC. To enable this; [configure your VPC to support](https://docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-private-apis.html) then set the `endpoint_configuration` to `PRIVATE` and set up Resource Policy on the API Gateway. A note about this; if you're using a private endpoint, Zappa won't be able to tell if the API is returning a successful status code upon deploy or update, so you'll have to check it manually to ensure your setup is working properly.
  For full list of options for endpoint configuration refer to [API Gateway EndpointConfiguration documentation](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-apigateway-restapi-endpointconfiguration.html)

  zappa_settings.json:
  ```json
  {
      "dev": {
          "endpoint_configuration": ["PRIVATE"],
          "apigateway_policy": "apigateway_resource_policy.json",
      },
  }
  ```
  apigateway_resource_policy.json:
  ```json
  {
      "Version": "2012-10-17",
      "Statement": [
          {
              "Effect": "Deny",
              "Principal": "*",
              "Action": "execute-api:Invoke",
              "Resource": "execute-api:/*",
              "Condition": {
                  "StringNotEquals": {
                      "aws:sourceVpc": "{{vpcID}}" // UPDATE ME
                  }
              }
          },
          {
              "Effect": "Allow",
              "Principal": "*",
              "Action": "execute-api:Invoke",
              "Resource": "execute-api:/*"
          }
      ]
  }
  ```

  Lambda may provide additional resources than provisioned during cold start initialization. Set `INSTANTIATE_LAMBDA_HANDLER_ON_IMPORT=True` to instantiate the lambda handler on import. This is an experimental feature - if startup time is critical, look into using Provisioned Concurrency.

  * [Django-Zappa tutorial (screencast)](https://www.youtube.com/watch?v=plUrbPN0xc8&feature=youtu.be).
  * [Using Django-Zappa, Part 1](https://serverlesscode.com/post/zappa-wsgi-for-python/).
  * [Using Django-Zappa, Part 2: VPCs](https://serverlesscode.com/post/zappa-wsgi-for-python-pt-2/).
  * [Building Serverless Microservices with Zappa and Flask](https://gun.io/blog/serverless-microservices-with-zappa-and-flask/)
  * [Zappa で Hello World するまで (Japanese)](http://qiita.com/satoshi_iwashita/items/505492193317819772c7)
  * [How to Deploy Zappa with CloudFront, RDS and VPC](https://jinwright.net/how-deploy-serverless-wsgi-app-using-zappa/)
  * [Secure 'Serverless' File Uploads with AWS Lambda, S3, and Zappa](http://blog.stratospark.com/secure-serverless-file-uploads-with-aws-lambda-s3-zappa.html)
  * [Deploy a Serverless WSGI App using Zappa, CloudFront, RDS, and VPC](https://docs.google.com/presentation/d/1aYeOMgQl4V_fFgT5VNoycdXtob1v6xVUWlyxoTEiTw0/edit#slide=id.p)
  * [AWS: Deploy Alexa Ask Skills with Flask-Ask and Zappa](https://developer.amazon.com/blogs/post/8e8ad73a-99e9-4c0f-a7b3-60f92287b0bf/New-Alexa-Tutorial-Deploy-Flask-Ask-Skills-to-AWS-Lambda-with-Zappa)
  * [Guide to using Django with Zappa](https://edgarroman.github.io/zappa-django-guide/)
  * [Zappa and LambCI](https://seancoates.com/blogs/zappa-and-lambci/)
  * [Building A Serverless Image Processing SaaS using Zappa](https://medium.com/99serverless/building-a-serverless-image-processing-saas-9ef68b594076)
  * [Serverless Slack Slash Commands with Python and Zappa](https://renzo.lucioni.xyz/serverless-slash-commands-with-python/)
  * [Bringing Tokusatsu to AWS using Python, Flask, Zappa and Contentful](https://www.contentful.com/blog/2018/03/07/bringing-tokusatsu-to-aws-using-python-flask-zappa-and-contentful/)
  * [AWS Summit 2018 Seoul - Zappa와 함께하는 Serverless Microservice](https://www.slideshare.net/YunSeopSong/zappa-serverless-microservice-94410308/)
  * [Book - Building Serverless Python Web Services with Zappa](https://github.com/PacktPublishing/Building-Serverless-Python-Web-Services-with-Zappa)
  * [Vider sa flask dans une lambda](http://free_zed.gitlab.io/articles/2019/11/vider-sa-flask-dans-une-lambda/)[French]
  * _Your guide here?_

  * _[Zappa Serves Python, Minus the Servers](http://www.infoworld.com/article/3031665/application-development/zappa-serves-python-web-apps-minus-the-servers.html)_
  * _[Zappa lyfter serverlösa applikationer med Python](http://computersweden.idg.se/2.2683/1.649895/zappa-lyfter-python)_
  * _[Interview: Rich Jones on Zappa](https://serverlesscode.com/post/rich-jones-interview-django-zappa/)_
  * [Top 10 Python Libraries of 2016](https://tryolabs.com/blog/2016/12/20/top-10-python-libraries-of-2016/)

  * [Mailchimp Signup Utility](https://github.com/sasha42/Mailchimp-utility) - A microservice for adding people to a mailing list via API.
  * [Zappa Slack Inviter](https://github.com/Miserlou/zappa-slack-inviter) - A tiny, server-less service for inviting new users to your Slack channel.
  * [Serverless Image Host](https://github.com/Miserlou/serverless-imagehost) - A thumbnailing service with Flask, Zappa and Pillow.
  * [Zappa BitTorrent Tracker](https://github.com/Miserlou/zappa-bittorrent-tracker) - An experimental server-less BitTorrent tracker. Work in progress.
  * [JankyGlance](https://github.com/Miserlou/JankyGlance) - A server-less Yahoo! Pipes replacement.
  * [LambdaMailer](https://github.com/tryolabs/lambda-mailer) - A server-less endpoint for processing a contact form.
  * [Voter Registration Microservice](https://topics.arlingtonva.us/2016/11/voter-registration-search-microservice/) - Official backup to to the Virginia Department of Elections portal.
  * [FreePoll Online](https://www.freepoll.online) - A simple and awesome say for groups to make decisions.
  * [PasteOfCode](https://paste.ofcode.org/) - A Zappa-powered paste bin.
  * And many more, including banks, governments, startups, enterprises and schools!
  Are you using Zappa? Let us know and we'll list your site here!

  * [Mackenzie](http://github.com/Miserlou/Mackenzie) - AWS Lambda Infection Toolkit
  * [NoDB](https://github.com/Miserlou/NoDB) - A simple, server-less, Pythonic object store based on S3.
  * [zappa-cms](http://github.com/Miserlou/zappa-cms) - A tiny server-less CMS for busy hackers. Work in progress.
  * [zappa-django-utils](https://github.com/Miserlou/zappa-django-utils) - Utility commands to help Django deployments.
  * [flask-ask](https://github.com/johnwheeler/flask-ask) - A framework for building Amazon Alexa applications. Uses Zappa for deployments.
  * [zappa-file-widget](https://github.com/anush0247/zappa-file-widget) - A Django plugin for supporting binary file uploads in Django on Zappa.
  * [zops](https://github.com/bjinwright/zops) - Utilities for teams and continuous integrations using Zappa.
  * [cookiecutter-mobile-backend](https://github.com/narfman0/cookiecutter-mobile-backend/) - A `cookiecutter` Django project with Zappa and S3 uploads support.
  * [zappa-examples](https://github.com/narfman0/zappa-examples/) - Flask, Django, image uploads, and more!
  * [zappa-hug-example](https://github.com/mcrowson/zappa-hug-example) - Example of a Hug application using Zappa.
  * [Zappa Docker Image](https://github.com/danielwhatmuff/zappa) - A Docker image for running Zappa locally, based on Lambda Docker.
  * [zappa-dashing](https://github.com/nikos/zappa-dashing) - Monitor your AWS environment (health/metrics) with Zappa and CloudWatch.
  * [s3env](https://github.com/cameronmaske/s3env) - Manipulate a remote Zappa environment variable key/value JSON object file in an S3 bucket through the CLI.
  * [zappa_resize_image_on_fly](https://github.com/wobeng/zappa_resize_image_on_fly) - Resize images on the fly using Flask, Zappa, Pillow, and OpenCV-python.
  * [zappa-ffmpeg](https://github.com/ubergarm/zappa-ffmpeg) - Run ffmpeg inside a lambda for serverless transformations.
  * [gdrive-lambda](https://github.com/richiverse/gdrive-lambda) - pass json data to a csv file for end users who use Gdrive across the organization.
  * [travis-build-repeat](https://github.com/bcongdon/travis-build-repeat) - Repeat TravisCI builds to avoid stale test results.
  * [wunderskill-alexa-skill](https://github.com/mcrowson/wunderlist-alexa-skill) - An Alexa skill for adding to a Wunderlist.
  * [xrayvision](https://github.com/mathom/xrayvision) - Utilities and wrappers for using AWS X-Ray with Zappa.
  * [terraform-aws-zappa](https://github.com/dpetzold/terraform-aws-zappa) - Terraform modules for creating a VPC, RDS instance, ElastiCache Redis and CloudFront Distribution for use with Zappa.
  * [zappa-sentry](https://github.com/jneves/zappa-sentry) - Integration with Zappa and Sentry
  * [IOpipe](https://github.com/iopipe/iopipe-python#zappa) - Monitor, profile and analyze your Zappa apps.

  Zappa goes quite far beyond what Lambda and API Gateway were ever intended to handle. As a result, there are quite a few hacks in here that allow it to work. Some of those include, but aren't limited to..
  * Using VTL to map body, headers, method, params and query strings into JSON, and then turning that into valid WSGI.
  * Attaching response codes to response bodies, Base64 encoding the whole thing, using that as a regex to route the response code, decoding the body in VTL, and mapping the response body to that.
  * Packing and _Base58_ encoding multiple cookies into a single cookie because we can only map one kind.
  * Forcing the case permutations of "Set-Cookie" in order to return multiple headers at the same time.
  * Turning cookie-setting 301/302 responses into 200 responses with HTML redirects, because we have no way to set headers on redirects.

  This project is still young, so there is still plenty to be done. Contributions are more than welcome!
  Please file tickets for discussion before submitting patches. Pull requests should target `master` and should leave Zappa in a "shippable" state if merged.
  If you are adding a non-trivial amount of new code, please include a functioning test in your PR. For AWS calls, we use the `placebo` library, which you can learn to use [in their README](https://github.com/garnaat/placebo#usage-as-a-decorator). The test suite will be run by [Travis CI](https://travis-ci.org/zappa/Zappa) once you open a pull request.
  Please include the GitHub issue or pull request URL that has discussion related to your changes as a comment in the code ([example](https://github.com/zappa/Zappa/blob/fae2925431b820eaedf088a632022e4120a29f89/zappa/zappa.py#L241-L243)). This greatly helps for project maintainability, as it allows us to trace back use cases and explain decision making. Similarly, please make sure that you meet all of the requirements listed in the [pull request template](https://raw.githubusercontent.com/zappa/Zappa/master/.github/PULL_REQUEST_TEMPLATE.md).
  Please feel free to work on any open ticket, especially any ticket marked with the "help-wanted" label. If you get stuck or want to discuss an issue further, please join [our Slack channel](https://zappateam.slack.com/), where you'll find a community of smart and interesting people working dilligently on hard problems.
  [Zappa Slack Auto Invite](https://slackautoinviter.herokuapp.com)
  Zappa does not intend to conform to PEP8, isolate your commits so that changes to functionality with changes made by your linter.

  To use the git HEAD, you *probably can't* use `pip install -e `. Instead, you should clone the repo to your machine and then `pip install /path/to/zappa/repo` or `ln -s /path/to/zappa/repo/zappa zappa` in your local project.

  If you or your company uses **Zappa**, please consider giving what you can to support the ongoing development of the project!
  You can become a patron by **[visiting our Patreon page](https://patreon.com/zappa)**.
  Zappa is currently supported by these awesome individuals and companies:
    * Nathan Lawrence
    * LaunchLab
    * Sean Paley
    * Theo Chitayat
    * George Sibble
    * Joe Weiss
    * Nik Bora
    * Zerong Toby Wang
    * Gareth E
    * Matt Jackson
    * Sean Coates
    * Alexander Loschilov
    * Korey Peters
    * Joe Weiss
    * Kimmo Parvianen-Jalanko
    * Patrick Agin
    * Roberto Martinez
    * Charles Dimino
    * Doug Beney
    * Dan "The Man" Gayle
    * Juancito
    * Will Childs-Klein
    * Efi Merdler Kravitz
    * **Philippe Trounev**
  Thank you very, very much!

  Do you need help with..
    * Porting existing Flask and Django applications to Zappa?
    * Building new applications and services that scale infinitely?
    * Reducing your operations and hosting costs?
    * Adding new custom features into Zappa?
    * Training your team to use AWS and other server-less paradigms?
  Good news! We're currently available for remote and on-site consulting for small, large and enterprise teams. Please contact <miserlou@gmail.com> with your needs and let's work together!
  <br />
  <p align="center">
    <a href="https://gun.io"><img src="http://i.imgur.com/M7wJipR.png" alt="Made by Gun.io"/></a>
  </p>


pkgId: e8cb3513d398132e0750907128a92491c9286b32950e2701fde9505afc71bc77
name: python-zarr
arch: src
version: 2.14.2
epoch: 0
release: 1
summary: An implementation of chunked, compressed, N-dimensional arrays for Python
url: https://github.com/zarr-developers/zarr-python
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-a380d543-726e-4b91-8afc-5677e817cbe0
location_href: 00005538-python-zarr/python-zarr-2.14.2-1.src.rpm
checksum_type: sha256
description:
  <div align="center">
    <img src="https://raw.githubusercontent.com/zarr-developers/community/main/logos/logo2.png"><br>
  </div>



  <table>
  <tr>
    <td>Latest Release</td>
    <td>
      <a href="https://pypi.org/project/zarr/">
      <img src="https://badge.fury.io/py/zarr.svg" alt="latest release" />
      </a>
    </td>
  </tr>
    <td></td>
    <td>
      <a href="https://anaconda.org/anaconda/zarr/">
      <img src="https://anaconda.org/conda-forge/zarr/badges/version.svg" alt="latest release" />
      </a>
  </td>
  </tr>
  <tr>
    <td>Package Status</td>
    <td>
  		<a href="https://pypi.org/project/zarr/">
  		<img src="https://img.shields.io/pypi/status/zarr.svg" alt="status" />
  		</a>
    </td>
  </tr>
  <tr>
    <td>License</td>
    <td>
      <a href="https://github.com/zarr-developers/zarr-python/blob/main/LICENSE.txt">
      <img src="https://img.shields.io/pypi/l/zarr.svg" alt="license" />
      </a>
  </td>
  </tr>
  <tr>
    <td>Build Status</td>
    <td>
      <a href="https://github.com/zarr-developers/zarr-python/blob/main/.github/workflows/python-package.yml">
      <img src="https://github.com/zarr-developers/zarr-python/actions/workflows/python-package.yml/badge.svg" alt="build status" />
      </a>
    </td>
  </tr>
  <tr>
    <td>Pre-commit Status</td>
    <td>
      <a href=""https://github.com/zarr-developers/zarr-python/blob/main/.pre-commit-config.yaml">
      <img src="https://results.pre-commit.ci/badge/github/zarr-developers/zarr-python/main.svg" alt="pre-commit status" />
      </a>
    </td>
  </tr>

  <tr>
    <td>Coverage</td>
    <td>
      <a href="https://codecov.io/gh/zarr-developers/zarr-python">
      <img src="https://codecov.io/gh/zarr-developers/zarr-python/branch/main/graph/badge.svg"/ alt="coverage">
      </a>
    </td>
  </tr>
  <tr>
    <td>Downloads</td>
    <td>
      <a href="https://zarr.readthedocs.io">
      <img src="https://pepy.tech/badge/zarr" alt="pypi downloads" />
      </a>
    </td>
  </tr>
  <tr>
  	<td>Gitter</td>
  	<td>
  		<a href="https://gitter.im/zarr-developers/community">
  		<img src="https://badges.gitter.im/zarr-developers/community.svg" />
  		</a>
  	</td>
  </tr>
  <tr>
  	<td>Citation</td>
  	<td>
  		<a href="https://doi.org/10.5281/zenodo.3773450">
  			<img src="https://zenodo.org/badge/DOI/10.5281/zenodo.3773450.svg" alt="DOI">
  		</a>
  	</td>
  </tr>

  </table>



  Zarr is a Python package providing an implementation of compressed, chunked, N-dimensional arrays, designed for use in parallel computing. See the [documentation](https://zarr.readthedocs.io) for more information.



  - [**Create**](https://zarr.readthedocs.io/en/stable/tutorial.html#creating-an-array) N-dimensional arrays with any NumPy `dtype`.
  - [**Chunk arrays**](https://zarr.readthedocs.io/en/stable/tutorial.html#chunk-optimizations) along any dimension.
  - [**Compress**](https://zarr.readthedocs.io/en/stable/tutorial.html#compressors) and/or filter chunks using any NumCodecs codec.
  - [**Store arrays**](https://zarr.readthedocs.io/en/stable/tutorial.html#tutorial-storage) in memory, on disk, inside a zip file, on S3, etc...
  - [**Read**](https://zarr.readthedocs.io/en/stable/tutorial.html#reading-and-writing-data) an array [**concurrently**](https://zarr.readthedocs.io/en/stable/tutorial.html#parallel-computing-and-synchronization) from multiple threads or processes.
  - Write to an array concurrently from multiple threads or processes.
  - Organize arrays into hierarchies via [**groups**](https://zarr.readthedocs.io/en/stable/tutorial.html#groups).



  Zarr can be installed from PyPI using `pip`:

  ```bash
  pip install zarr
  ```

  or via `conda`:

  ```bash
  conda install -c conda-forge zarr
  ```

  For more details, including how to install from source, see the [installation documentation](https://zarr.readthedocs.io/en/stable/index.html#installation).


pkgId: 15ab35d0b682fc92bb733cf2b1c6b09f6b4d8049c544568e4b417655dc157e91
name: python-zc.recipe.egg
arch: src
version: 2.0.7
epoch: 0
release: 1
summary: Recipe for installing Python package distributions as eggs
url: http://cheeseshop.python.org/pypi/zc.recipe.egg
rpm_license: ZPL 2.1
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-2136e6e7-c74d-41df-bf24-76eb77cae109
location_href: 00012147-python-zc-recipe-egg/python-zc.recipe.egg-2.0.7-1.src.rpm
checksum_type: sha256
description:
  - For the 2.0.6 change, we require zc.buildout 2.12.0. The `install_requires`
    in `setup.py` now also says that.


pkgId: 95e0d9833f17b045eb76db24fe041887e6e0117e30fef90cee1c9c17943d9201
name: python-zeroconf
arch: src
version: 0.47.3
epoch: 0
release: 1
summary: A pure python implementation of multicast DNS service discovery
url: https://github.com/python-zeroconf/python-zeroconf
rpm_license: LGPL
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-f9c81f8b-8fdb-4738-a694-37f301a368f1
location_href: 00005543-python-zeroconf/python-zeroconf-0.47.3-1.src.rpm
checksum_type: sha256
description:
  `Documentation <https://python-zeroconf.readthedocs.io/en/latest/>`_.
  This is fork of pyzeroconf, Multicast DNS Service Discovery for Python,
  originally by Paul Scott-Murphy (https://github.com/paulsm/pyzeroconf),
  modified by William McBrine (https://github.com/wmcbrine/pyzeroconf).
  The original William McBrine's fork note::
      This fork is used in all of my TiVo-related projects: HME for Python
      (and therefore HME/VLC), Network Remote, Remote Proxy, and pyTivo.
      Before this, I was tracking the changes for zeroconf.py in three
      separate repos. I figured I should have an authoritative source.
      Although I make changes based on my experience with TiVos, I expect that
      they're generally applicable. This version also includes patches found
      on the now-defunct (?) Launchpad repo of pyzeroconf, and elsewhere
      around the net -- not always well-documented, sorry.
  Compatible with:
  * Bonjour
  * Avahi
  Compared to some other Zeroconf/Bonjour/Avahi Python packages, python-zeroconf:
  * isn't tied to Bonjour or Avahi
  * doesn't use D-Bus
  * doesn't force you to use particular event loop or Twisted (asyncio is used under the hood but not required)
  * is pip-installable
  * has PyPI distribution
  * has an optional cython extension for performance (pure python is supported as well)


pkgId: 61acf317570b63461fc6bba3a7895a4dcb102db157c06038aafed1cc4aaa5e4f
name: python-zerorpc
arch: src
version: 0.6.3
epoch: 0
release: 1
summary: zerorpc is a flexible RPC based on zeromq.
url: https://github.com/0rpc/zerorpc-python
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-11cc4bec-339f-4ced-97ea-4edc8fe6ffbf
location_href: 00013194-python-zerorpc/python-zerorpc-0.6.3-1.src.rpm
checksum_type: sha256
description:
  Mailing list: zerorpc@googlegroups.com (https://groups.google.com/d/forum/zerorpc)
  zerorpc is a flexible RPC implementation based on zeromq and messagepack.
  Service APIs exposed with zerorpc are called "zeroservices".
  zerorpc can be used programmatically or from the command-line. It comes
  with a convenient script, "zerorpc", allowing to:
  * expose Python modules without modifying a single line of code,
  * call those modules remotely through the command line.


pkgId: 42f25375e9e21b81f808e9a60852670e464bdcfbe299e7a20bb5af116a861bf8
name: python-zict
arch: src
version: 2.2.0
epoch: 0
release: 1
summary: Mutable mapping tools
url: http://zict.readthedocs.io/en/latest/
rpm_license: BSD
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-6e55b5af-b0cc-4f59-a101-669260940fba
location_href: 00005544-python-zict/python-zict-2.2.0-1.src.rpm
checksum_type: sha256
description:
  Mutable Mapping tools. See `documentation`_.

  .. _documentation: http://zict.readthedocs.io/en/latest/


pkgId: ca605665ea4327662c8552e5686fb70a3be095ecbf6a89f55c74158dd41ffdf1
name: python-zipcodes
arch: src
version: 1.2.0
epoch: 0
release: 1
summary: Query U.S. state zipcodes without SQLite.
url: https://github.com/seanpianka/zipcodes
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-7481a8e1-79a5-4610-8570-acda092762b2
location_href: 00011732-python-zipcodes/python-zipcodes-1.2.0-1.src.rpm
checksum_type: sha256
description:
  Zipcodes is a simple library for querying U.S. zipcodes.

  The Python `sqlite3` module is not required in order to use this package.

  ```python
  >>> import zipcodes
  >>> assert zipcodes.is_real('77429')
  >>> assert len(zipcodes.similar_to('7742')) != 0
  >>> exact_zip = zipcodes.matching('77429')[0]
  >>> filtered_zips = zipcodes.filter_by(city="Cypress", state="TX")
  >>> assert exact_zip in filtered_zips
  >>> pprint.pprint(exact_zip)
  {'acceptable_cities': [],
    'active': True,
    'area_codes': ['281', '832'],
    'city': 'Cypress',
    'country': 'US',
    'county': 'Harris County',
    'lat': '29.9857',
    'long': '-95.6548',
    'state': 'TX',
    'timezone': 'America/Chicago',
    'unacceptable_cities': [],
    'world_region': 'NA',
    'zip_code': '77429',
    'zip_code_type': 'STANDARD'}[
  ```

  ⚠️ The zipcode data was last updated on: **Oct. 3, 2021** ⚠️

  [![Downloads](https://pepy.tech/badge/zipcodes/month)](https://pepy.tech/project/zipcodes/month)
  [![Supported Versions](https://img.shields.io/pypi/pyversions/zipcodes.svg)](https://pypi.org/project/zipcodes)
  [![Contributors](https://img.shields.io/github/contributors/seanpianka/zipcodes.svg)](https://github.com/seanpianka/zipcodes/graphs/contributors)




  Zipcodes is available on PyPI:

  ```console
  $ python -m pip install zipcodes
  ```

  Zipcodes supports Python 2.6+ and Python 3.2+.



  Add a data file to your PyInstaller bundle with the [`--add-data`](https://pyinstaller.readthedocs.io/en/stable/spec-files.html#adding-data-files) flag.



  `--add-data "<path-to-package-install>/zipcodes/zips.json.bz2:zipcodes"`


  `--add-data "<path-to-package-install>\zipcodes\zips.json.bz2;zipcodes"`



  The build script for the zipcode data outputs a JSON file containing all the zipcode data and zipped using bzip2. The data sources are stored under `build/app/data`.

  Build the zipcode data for distribution:

  ```shell script
  $ build/app/__init__.py # outputs `zipcodes/zips.json.bz2`
  ```




  The tests are defined in a declarative, table-based format that generates test
  cases.

  Run the tests directly:

  ```shell script
  $ python tests/__init__.py
  ```



  ```python
  >>> from pprint import pprint
  >>> import zipcodes

  >>> # Simple zip-code matching.
  >>> pprint(zipcodes.matching('77429'))
  [{'acceptable_cities': [],
    'active': True,
    'area_codes': ['281', '832'],
    'city': 'Cypress',
    'country': 'US',
    'county': 'Harris County',
    'lat': '29.9857',
    'long': '-95.6548',
    'state': 'TX',
    'timezone': 'America/Chicago',
    'unacceptable_cities': [],
    'world_region': 'NA',
    'zip_code': '77429',
    'zip_code_type': 'STANDARD'}]


  >>> # Handles of Zip+4 zip-codes nicely. :)
  >>> pprint(zipcodes.matching('77429-1145'))
  [{'acceptable_cities': [],
    'active': True,
    'area_codes': ['281', '832'],
    'city': 'Cypress',
    'country': 'US',
    'county': 'Harris County',
    'lat': '29.9857',
    'long': '-95.6548',
    'state': 'TX',
    'timezone': 'America/Chicago',
    'unacceptable_cities': [],
    'world_region': 'NA',
    'zip_code': '77429',
    'zip_code_type': 'STANDARD'}]

  >>> # Will try to handle invalid zip-codes gracefully...
  >>> print(zipcodes.matching('06463'))
  []

  >>> # Until it cannot.
  >>> zipcodes.matching('0646a')
  Traceback (most recent call last):
    ...
  TypeError: Invalid characters, zipcode may only contain digits and "-".

  >>> zipcodes.matching('064690')
  Traceback (most recent call last):
    ...
  TypeError: Invalid format, zipcode must be of the format: "#####" or "#####-####"

  >>> zipcodes.matching(None)
  Traceback (most recent call last):
    ...
  TypeError: Invalid type, zipcode must be a string.

  >>> # Whether the zip-code exists within the database.
  >>> print(zipcodes.is_real('06463'))
  False

  >>> # How handy!
  >>> print(zipcodes.is_real('06469'))
  True

  >>> # Search for zipcodes that begin with a pattern.
  >>> pprint(zipcodes.similar_to('1018'))
  [{'acceptable_cities': [],
    'active': False,
    'area_codes': ['212'],
    'city': 'New York',
    'country': 'US',
    'county': 'New York County',
    'lat': '40.71',
    'long': '-74',
    'state': 'NY',
    'timezone': 'America/New_York',
    'unacceptable_cities': ['J C Penney'],
    'world_region': 'NA',
    'zip_code': '10184',
    'zip_code_type': 'UNIQUE'},
   {'acceptable_cities': [],
    'active': True,
    'area_codes': ['212'],
    'city': 'New York',
    'country': 'US',
    'county': 'New York County',
    'lat': '40.7143',
    'long': '-74.0067',
    'state': 'NY',
    'timezone': 'America/New_York',
    'unacceptable_cities': [],
    'world_region': 'NA',
    'zip_code': '10185',
    'zip_code_type': 'PO BOX'}]

  >>> # Use filter_by to filter a list of zip-codes by specific attribute->value pairs.
  >>> pprint(zipcodes.filter_by(city="Old Saybrook"))
  [{'acceptable_cities': [],
    'active': True,
    'area_codes': ['860'],
    'city': 'Old Saybrook',
    'country': 'US',
    'county': 'Middlesex County',
    'lat': '41.3015',
    'long': '-72.3879',
    'state': 'CT',
    'timezone': 'America/New_York',
    'unacceptable_cities': ['Fenwick'],
    'world_region': 'NA',
    'zip_code': '06475',
    'zip_code_type': 'STANDARD'}]

  >>> # Arbitrary nesting of similar_to and filter_by calls, allowing for great precision while filtering.
  >>> pprint(zipcodes.similar_to('2', zips=zipcodes.filter_by(active=True, city='Windsor')))
  [{'acceptable_cities': [],
    'active': True,
    'area_codes': ['757'],
    'city': 'Windsor',
    'country': 'US',
    'county': 'Isle of Wight County',
    'lat': '36.8628',
    'long': '-76.7143',
    'state': 'VA',
    'timezone': 'America/New_York',
    'unacceptable_cities': [],
    'world_region': 'NA',
    'zip_code': '23487',
    'zip_code_type': 'STANDARD'},
   {'acceptable_cities': ['Askewville'],
    'active': True,
    'area_codes': ['252'],
    'city': 'Windsor',
    'country': 'US',
    'county': 'Bertie County',
    'lat': '35.9942',
    'long': '-76.9422',
    'state': 'NC',
    'timezone': 'America/New_York',
    'unacceptable_cities': [],
    'world_region': 'NA',
    'zip_code': '27983',
    'zip_code_type': 'STANDARD'},
   {'acceptable_cities': [],
    'active': True,
    'area_codes': ['803'],
    'city': 'Windsor',
    'country': 'US',
    'county': 'Aiken County',
    'lat': '33.4730',
    'long': '-81.5132',
    'state': 'SC',
    'timezone': 'America/New_York',
    'unacceptable_cities': [],
    'world_region': 'NA',
    'zip_code': '29856',
    'zip_code_type': 'STANDARD'}]

  >>> # Have any other ideas? Make a pull request and start contributing today!
  >>> # Made with love by Sean Pianka
  ```


pkgId: 4196729d33102dc108c3310889f1e8e3af3ca33d6d3391da96b63209b1ef20f6
name: python-zipfile2
arch: src
version: 0.0.12
epoch: 0
release: 1
summary: An improved ZipFile class.
url: https://pypi.org/project/zipfile2/
rpm_license: PSFL
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-ccae0a1d-85df-47e3-b749-4fe243fb32aa
location_href: 00013631-python-zipfile2/python-zipfile2-0.0.12-1.src.rpm
checksum_type: sha256
description:
  .. image:: https://travis-ci.org/cournape/zipfile2.png?branch=master
      :target: https://travis-ci.org/cournape/zipfile2

  zipfile2 contains an improved ZipFile class that may be used as a 100 %
  backward compatible replacement.

  Improvements compared to upstream zipfile stdlib:

  * Handling of symlinks (read and write)
  * Compatible 2.6 onwards (including 3.x), include context manager
  * Raises an exception by default when duplicate members are detected.
  * Special class `LeanZipFile` to avoid using too much memory when handling
    zip files with a large number of members. Contrary to the stdlib
    ZipFile, it does not create the list of all archives when opening the
    file. This can save 100s of MB for zipfiles with a large number of
    members.


pkgId: 6ad9c9c77dd6dc08caee7a6a0a9fb52f34068be83cb869814591ab89bdc9d7c9
name: python-zipfile36
arch: src
version: 0.1.3
epoch: 0
release: 1
summary: Read and write ZIP files - backport of the zipfile module from Python 3.6
url: https://gitlab.com/takluyver/zipfile36
rpm_license: Python Software Foundation License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-e4260e00-2be5-4ae6-be31-4a3885af220b
location_href: 00012656-python-zipfile36/python-zipfile36-0.1.3-1.src.rpm
checksum_type: sha256
description:
  This is a backport of the zipfile module from Python 3.6, which contains some
  improvements.

  Installation::

      pip install zipfile36

  Suggested usage:

  .. code:: python

      if sys.version_info >= (3, 6):
          import zipfile
      else:
          import zipfile36 as zipfile


pkgId: 0ac7be6e6f54b13a0d6c7db78b76e841f9a153f8c2d2532660e237ec2a36f9b8
name: python-zipfile38
arch: src
version: 0.0.3
epoch: 0
release: 1
summary: Read and write ZIP files - backport of the zipfile module from Python 3.8
url: https://github.com/lorencarvalho/zipfile38
rpm_license: Python Software Foundation License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-233f3a0e-05c7-4e77-917e-0a07ed655b13
location_href: 00010113-python-zipfile38/python-zipfile38-0.0.3-1.src.rpm
checksum_type: sha256
description:
  shout out to `Thomas Kluyver's backport for 3.6 <https://gitlab.com/takluyver/zipfile36>`_ and `Markus Scheidgen's backport for 3.7 <https://github.com/markus1978/zipfile37>`_.


pkgId: 4b1e36a5098a924f3ed54af968da300edc8116c022ad8f90453c486c817c895d
name: python-zipstream-new
arch: src
version: 1.1.8
epoch: 0
release: 1
summary: Zipfile generator that takes input files as well as streams
url: https://github.com/arjan-s/python-zipstream
rpm_license: GNU General Public License v3 (GPLv3)
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-1171823a-29a7-4e77-9948-c545c53a4f63
location_href: 00012473-python-zipstream-new/python-zipstream-new-1.1.8-1.src.rpm
checksum_type: sha256
description:
  zipstream.py is a zip archive generator based on python 3.3's zipfile.py. It was created to
  generate a zip file generator for streaming (ie web apps). This is beneficial for when you
  want to provide a downloadable archive of a large collection of regular files, which would be infeasible to
  generate the archive prior to downloading or of a very large file that you do not want to store entirely on disk or on memory.

  The archive is generated as an iterator of strings, which, when joined, form
  the zip archive. For example, the following code snippet would write a zip
  archive containing files from 'path' to a normal file:

  ```python
  import zipstream

  z = zipstream.ZipFile()
  z.write('path/to/files')

  with open('zipfile.zip', 'wb') as f:
      for data in z:
          f.write(data)
  ```

  zipstream also allows to take as input a byte string iterable and to generate
  the archive as an iterator.
  This avoids storing large files on disk or in memory.
  To do so you could use something like this snippet:

  ```python
  def iterable():
      for _ in xrange(10):
          yield b'this is a byte string\x01\n'

  z = zipstream.ZipFile()
  z.write_iter('my_archive_iter', iterable())

  with open('zipfile.zip', 'wb') as f:
      for data in z:
          f.write(data)
  ```

  Of course both approach can be combined:

  ```python
  def iterable():
      for _ in xrange(10):
          yield b'this is a byte string\x01\n'

  z = zipstream.ZipFile()
  z.write('path/to/files', 'my_archive_files')
  z.write_iter('my_archive_iter', iterable())

  with open('zipfile.zip', 'wb') as f:
      for data in z:
          f.write(data)
  ```

  Since recent versions of web.py support returning iterators of strings to be
  sent to the browser, to download a dynamically generated archive, you could
  use something like this snippet:

  ```python
  def GET(self):
      path = '/path/to/dir/of/files'
      zip_filename = 'files.zip'
      web.header('Content-type' , 'application/zip')
      web.header('Content-Disposition', 'attachment; filename="%s"' % (
          zip_filename,))
      return zipstream.ZipFile(path)
  ```

  If the zlib module is available, zipstream.ZipFile can generate compressed zip
  archives.



  ```
  pip install zipstream-new
  ```



    * Python 2.6+, 3.2+, pypy





  ```python
  from flask import Response

  @app.route('/package.zip', methods=['GET'], endpoint='zipball')
  def zipball():
      def generator():
          z = zipstream.ZipFile(mode='w', compression=zipstream.ZIP_DEFLATED)

          z.write('/path/to/file')

          for chunk in z:
              yield chunk

      response = Response(generator(), mimetype='application/zip')
      response.headers['Content-Disposition'] = 'attachment; filename={}'.format('files.zip')
      return response



  @app.route('/package.zip', methods=['GET'], endpoint='zipball')
  def zipball():
      z = zipstream.ZipFile(mode='w', compression=zipstream.ZIP_DEFLATED)
      z.write('/path/to/file')

      response = Response(z, mimetype='application/zip')
      response.headers['Content-Disposition'] = 'attachment; filename={}'.format('files.zip')
      return response



  @app.route('/package.zip', methods=['GET'], endpoint='zipball')
  def zipball():
      def generate_zip_with_manifest():
          z = zipstream.ZipFile(mode='w', compression=zipstream.ZIP_DEFLATED)

          manifest = []
          for filename in os.listdir('/path/to/files'):
              z.write(os.path.join('/path/to/files', filename), arcname=filename)
              yield from z.flush()
              manifest.append(filename)

          z.write_str('manifest.json', json.dumps(manifest).encode())

          yield from z

      response = Response(z, mimetype='application/zip')
      response.headers['Content-Disposition'] = 'attachment; filename={}'.format('files.zip')
      return response
  ```



  ```python
  from django.http import StreamingHttpResponse

  def zipball(request):
      z = zipstream.ZipFile(mode='w', compression=zipstream.ZIP_DEFLATED)
      z.write('/path/to/file')

      response = StreamingHttpResponse(z, content_type='application/zip')
      response['Content-Disposition'] = 'attachment; filename={}'.format('files.zip')
      return response
  ```



  ```python
  def GET(self):
      path = '/path/to/dir/of/files'
      zip_filename = 'files.zip'
      web.header('Content-type' , 'application/zip')
      web.header('Content-Disposition', 'attachment; filename="%s"' % (
          zip_filename,))
      return zipstream.ZipFile(path)
  ```



  With python version > 2.6, just run the following command: `python -m unittest discover`

  Alternatively, you can use `nose`.

  If you want to run the tests on all supported Python versions, run `tox`.


pkgId: 0a465ebdd8ec4ff8a3e74498e8b91e1b99c3bc8afa8ce1916ceeb042ed860188
name: python-zodbpickle
arch: src
version: 2.6
epoch: 0
release: 1
summary: Fork of Python 2 and 3 pickle module.
url: https://github.com/zopefoundation/zodbpickle
rpm_license: PSFL 2 and ZPL 2.1
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-4a56478c-e49e-46c4-8486-d4d6256ad5ab
location_href: 00003247-python-zodbpickle/python-zodbpickle-2.6-1.src.rpm
checksum_type: sha256
description:
  This package presents a uniform pickling interface for ZODB:
  - Under Python2, this package forks both Python 2.7's ``pickle`` and
    ``cPickle`` modules, adding support for the ``protocol 3`` opcodes.
    It also provides a new subclass of ``bytes``, ``zodbpickle.binary``,
    which Python2 applications can use to pickle binary values such that
    they will be unpickled as ``bytes`` under Py3k.
  - Under Py3k, this package forks the ``pickle`` module (and the supporting
    C extension) from both Python 3.2 and Python 3.3.  The fork add support
    for the ``noload`` operations used by ZODB.


pkgId: eeb8b612360a55d85863b8ee997c986cb46e095c72e645d002ddba26d66126bd
name: python-zodbpickle-debuginfo
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summary: Debug information for package python-zodbpickle
url: https://github.com/zopefoundation/zodbpickle
rpm_license: PSFL 2 and ZPL 2.1
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Development/Debug
rpm_buildhost: resalloc-b43af0dc-5a5f-46e6-8a39-c51b8f163632
rpm_sourcerpm: python-zodbpickle-3.0.1-1.src.rpm
location_href: 00008866-python-zodbpickle/python-zodbpickle-debuginfo-3.0.1-1.x86_64.rpm
checksum_type: sha256
description:
  This package provides debug information for package python-zodbpickle.
  Debug information is useful when developing applications that use this
  package or when debugging this package.


pkgId: 7890c4d4d24cf5ab4976467c9f9970c36b467c680d4638a1d9047e42b6a79d6d
name: python-zodbpickle-debugsource
arch: x86_64
version: 3.0.1
epoch: 0
release: 1
summary: Debug sources for package python-zodbpickle
url: https://github.com/zopefoundation/zodbpickle
rpm_license: PSFL 2 and ZPL 2.1
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rpm_group: Development/Debug
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location_href: 00008866-python-zodbpickle/python-zodbpickle-debugsource-3.0.1-1.x86_64.rpm
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description:
  This package provides debug sources for package python-zodbpickle.
  Debug sources are useful when developing applications that use this
  package or when debugging this package.


pkgId: c4b8d26a09eb9ba53ddc0c0fb9be2786a558eedf1f1b21b3cf9d4a6dcca3cfec
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summary: Debug information for package python-zope.container
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checksum_type: sha256
description:
  This package provides debug information for package python-zope.container.
  Debug information is useful when developing applications that use this
  package or when debugging this package.


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location_href: 00012456-python-zope-container/python-zope.container-debugsource-5.0-1.x86_64.rpm
checksum_type: sha256
description:
  This package provides debug sources for package python-zope.container.
  Debug sources are useful when developing applications that use this
  package or when debugging this package.


pkgId: fcee5b6c3870a2eada6dd603a4a152557e8fafac21538cd8d4075ad10ab15401
name: python-zope.fixers
arch: src
version: 1.1.2
epoch: 0
release: 1
summary: 2to3 fixers for Zope
url: http://svn.zope.org/zope.fixers/
rpm_license: ZPL
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-72d0e261-24ac-4a9e-96c7-aea7f59aba6f
location_href: 00005551-python-zope-fixers/python-zope.fixers-1.1.2-1.src.rpm
checksum_type: sha256
description:
  Fixers for Zope Component Architecture and the frameworks built with it.
  Currently, there is only one fixer, fix_implements. This fixer will change
  all uses of implements(IFoo) in a class body to the class decorator
  @implementer(IFoo), which is the most likely Python 3 syntax for
  zope.interfaces implements statements.


pkgId: 7877940f785ce7fb020ace61b0770d9f45055039299ce765cd09e75806d106ba
name: python-zope.hookable-debuginfo
arch: x86_64
version: 5.4
epoch: 0
release: 1
summary: Debug information for package python-zope.hookable
url: http://github.com/zopefoundation/zope.hookable
rpm_license: ZPL 2.1
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Development/Debug
rpm_buildhost: resalloc-edc169d1-be93-4444-891b-b7cac61d7f27
rpm_sourcerpm: python-zope.hookable-5.4-1.src.rpm
location_href: 00010468-python-zope-hookable/python-zope.hookable-debuginfo-5.4-1.x86_64.rpm
checksum_type: sha256
description:
  This package provides debug information for package python-zope.hookable.
  Debug information is useful when developing applications that use this
  package or when debugging this package.


pkgId: b2b298625f602e760249d87d54806afce13bfdc0be320e18291639d32a3cd705
name: python-zope.hookable-debugsource
arch: x86_64
version: 5.4
epoch: 0
release: 1
summary: Debug sources for package python-zope.hookable
url: http://github.com/zopefoundation/zope.hookable
rpm_license: ZPL 2.1
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Development/Debug
rpm_buildhost: resalloc-edc169d1-be93-4444-891b-b7cac61d7f27
rpm_sourcerpm: python-zope.hookable-5.4-1.src.rpm
location_href: 00010468-python-zope-hookable/python-zope.hookable-debugsource-5.4-1.x86_64.rpm
checksum_type: sha256
description:
  This package provides debug sources for package python-zope.hookable.
  Debug sources are useful when developing applications that use this
  package or when debugging this package.


pkgId: bbc55cd91f248373510254fbae425eb1824bdf86a2002903a945a2a03f3bad7b
name: python-zope.i18nmessageid
arch: src
version: 5.1.1
epoch: 0
release: 1
summary: Message Identifiers for internationalization
url: https://github.com/zopefoundation/zope.i18nmessageid
rpm_license: ZPL 2.1
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-66f517ae-0db7-4af7-8abe-98dbc70b365b
location_href: 00005552-python-zope-i18nmessageid/python-zope.i18nmessageid-5.1.1-1.src.rpm
checksum_type: sha256
description:
  To translate any text, we must be able to discover the source domain
  of the text.  A source domain is an identifier that identifies a
  project that produces program source strings.  Source strings occur as
  literals in python programs, text in templates, and some text in XML
  data.  The project implies a source language and an application
  context.
  We can think of a source domain as a collection of messages and
  associated translation strings.
  We often need to create unicode strings that will be displayed by
  separate views.  The view cannot translate the string without knowing
  its source domain.  A string or unicode literal carries no domain
  information, therefore we use messages.  Messages are unicode strings
  which carry a translation source domain and possibly a default
  translation.  They are created by a message factory. The message
  factory is created by calling ``MessageFactory`` with the source
  domain.
  This package provides facilities for *declaring* such messages within
  program source text;  translation of the messages is the responsiblitiy
  of the 'zope.i18n' package.


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  Debug information is useful when developing applications that use this
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  Debug information is useful when developing applications that use this
  package or when debugging this package.


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name: python-zopfli
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summary: Zopfli module for python
url: https://github.com/fonttools/py-zopfli
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  `zopfli <http://googledevelopers.blogspot.com/2013/02/compress-data-more-densely-with-zopfli.html>`__.
  It requires Python 3.7 or greater.


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  Debug sources are useful when developing applications that use this
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  Debug information is useful when developing applications that use this
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  Debug sources are useful when developing applications that use this
  package or when debugging this package.


pkgId: aa8b1479742699d905a455f056d006124add9db6eaddba17df7a8def282dda62
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description: Please see the github repo and help @ https://github.com/LamaAni/zthreading.py
url: https://github.com/LamaAni/zthreading.py
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  A collection of wrapper classes for event broadcast and task management for python (Python Threads or Asyncio).


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  Zuul-client is a CLI tool that can be used to interact with Zuul, the project
  gating system.
  The latest documentation for Zuul and Zuul Client can be found at:
  https://zuul-ci.org/docs/


pkgId: ac2a31f38a3444b13c99ecdee052526c51b856f1b75f3c48afc569ce713091d1
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  `Zuul <https://zuul-ci.org>`__ jobs and configuration.


pkgId: 083d2066a6277f4a862537c80f89cbe6ce9ebcd4a628d6914bd5b95a47559f12
name: python-zxcvbn
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url: https://github.com/dwolfhub/zxcvbn-python
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rpm_group: Unspecified
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description:
  A realistic password strength estimator.
  This is a Python implementation of the library created by the team at Dropbox.
  The original library, written for JavaScript, can be found
  `here <https://github.com/dropbox/zxcvbn>`__.
  While there may be other Python ports available, this one is the most up
  to date and is recommended by the original developers of zxcvbn at this
  time.


pkgId: 5361f95b8dbb47e2d742d5fe75d6a412761e2af03cdf9cc624000a214887fac3
name: python-zxcvbn-python
arch: src
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epoch: 0
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summary: please add a summary manually as the author left a blank one
url: https://github.com/dwolfhub/zxcvbn-python
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description:
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  https://pypi.python.org/pypi/zxcvbn
  |Build Status|


pkgId: 9bae3bfaf0f9309494c1c3ddaefd6b55c1ee6d4ed0020ac077d3131b76ded2e6
name: python3-ARC-Alkali-Rydberg-Calculator
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version: 3.2.9
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release: 1
summary: Alkali Rydberg Calculator
url: https://github.com/nikolasibalic/ARC-Alkali-Rydberg-Calculator
rpm_license: BSD3
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-5a177356-0599-432d-b70b-22d05ef04ed3
rpm_sourcerpm: python-ARC-Alkali-Rydberg-Calculator-3.2.9-1.src.rpm
checksum_type: sha256
description:
  ARC (Alkali.ne Rydberg Calculator)  is package of routines written in Python, using object-oriented programming (OOP) to make modular, reusable and extendable collection of routines and data for performing useful calculations of single atom and two-atom properties, like level diagrams, interactions and transition strengths for alkali metal and divalent atoms.
  Start by installing the latest version of the ARC package calling Python pip ([see prerequisites](https://arc-alkali-rydberg-calculator.readthedocs.io/en/latest/installation.html)) from the command line:
  ```pip install ARC-Alkali-Rydberg-Calculator ```
  For documentation see [online documentation on Read The Docs](http://arc-alkali-rydberg-calculator.readthedocs.io).
  For examples of use check [IPython example notebooks](https://arc-alkali-rydberg-calculator.readthedocs.io/en/latest/getting_started.html#ipython-notebook-with-examples).
  For online access to a selection of package functions see [online Atom Calculator](https://atomcalc.org).
  If you want to contribute to the project, [check this page](https://arc-alkali-rydberg-calculator.readthedocs.io/en/latest/contribute.html).

location_href:
  00014065-python-arc-alkali-rydberg-calculator/python3-ARC-Alkali-Rydberg-Calculator-3.2.9-1.x86_64.rpm


pkgId: a06a640ac4a8b4d949bd936a22db999b360060843076676b24d8048ad470c716
name: python3-Office365-REST-Python-Client
arch: noarch
version: 2.4.0
epoch: 0
release: 1
summary: Microsoft 365 & Microsoft Graph Library for Python
url: https://github.com/vgrem/Office365-REST-Python-Client
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
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rpm_sourcerpm: python-Office365-REST-Python-Client-2.4.0-1.src.rpm
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description:
  Office 365 & Microsoft Graph library for Python



  1. [Installation](#Installation)
  2. [Working with SharePoint API](#Working-with-SharePoint-API)
  3. [Working with Outlook API](#Working-with-Outlook-API)
  4. [Working with OneDrive API](#Working-with-OneDrive-API)
  5. [Working with Teams API](#Working-with-Microsoft-Teams-API)
  6. [Working with OneNote API](#Working-with-Microsoft-OneNote-API)
  7. [Working with Planner API](#Working-with-Microsoft-Planner-API)


  [![Downloads](https://pepy.tech/badge/office365-rest-python-client/month)](https://pepy.tech/project/office365-rest-python-client)
  [![PyPI](https://img.shields.io/pypi/v/Office365-REST-Python-Client.svg)](https://pypi.python.org/pypi/Office365-REST-Python-Client)
  [![PyPI pyversions](https://img.shields.io/pypi/pyversions/Office365-REST-Python-Client.svg)](https://pypi.python.org/pypi/Office365-REST-Python-Client/)
  [![Build Status](https://travis-ci.com/vgrem/Office365-REST-Python-Client.svg?branch=master)](https://travis-ci.com/vgrem/Office365-REST-Python-Client)



  Use pip:

  ```
  pip install Office365-REST-Python-Client
  ```


  >

  >Alternatively the _latest_ version could be directly installed via GitHub:
  >```
  >pip install git+https://github.com/vgrem/Office365-REST-Python-Client.git
  >```


  For the following examples, relevant credentials can be found in the Azure Portal.

  Steps to access:
  1. Login to the home page of the Azure Portal
  2. Navigate to "Azure Active Directory" using the three bars in the top right corner of the portal
  3. Select "App registrations" in the navigation panel on the left
  4. Search for and select your relevant application
  5. In the application's "Overview" page, the client id can be found under "Application (client) id"
  6. In the application's "Certificates & Secrets" page, the client secret can be found under the "Value" of the "Client Secrets." If there is no client secret yet, create one here.



  The list of supported API versions:
  -   [SharePoint 2013 REST API](https://msdn.microsoft.com/en-us/library/office/jj860569.aspx) and above
  -   SharePoint Online & OneDrive for Business REST API



  The following auth flows are supported:

  - app principals flow:
    `ClientContext.with_credentials(client_credentials)`

    Usage:
    ```
    client_credentials = ClientCredential('{client_id}','{client_secret}')
    ctx = ClientContext('{url}').with_credentials(client_credentials)
    ```
    Documentation: refer [Granting access using SharePoint App-Only](https://docs.microsoft.com/en-us/sharepoint/dev/solution-guidance/security-apponly-azureacs) for a details

    Example: [connect_with_app_principal.py](examples/sharepoint/connect_with_app_only_principal.py)

  - user credentials flow: `ClientContext.with_credentials(user_credentials)`

    Usage:
    ```
    user_credentials = UserCredential('{username}','{password}')
    ctx = ClientContext('{url}').with_credentials(user_credentials)
    ```
    Example: [connect_with_user_credential.py](examples/sharepoint/connect_with_user_credential.py)

  - certificate credentials flow: `ClientContext.with_certificate(tenant, client_id, thumbprint, cert_path)`

    Documentation: [Granting access via Azure AD App-Only](https://docs.microsoft.com/en-us/sharepoint/dev/solution-guidance/security-apponly-azuread)

    Example: [connect_with_client_certificate.py](examples/sharepoint/connect_with_client_certificate.py)



  There are **two approaches** available to perform API queries:

  1. `ClientContext class` - where you target SharePoint resources such as `Web`, `ListItem` and etc (recommended)

  ```python
  from office365.runtime.auth.user_credential import UserCredential
  from office365.sharepoint.client_context import ClientContext
  site_url = "https://{your-tenant-prefix}.sharepoint.com"
  ctx = ClientContext(site_url).with_credentials(UserCredential("{username}", "{password}"))
  web = ctx.web
  ctx.load(web)
  ctx.execute_query()
  print("Web title: {0}".format(web.properties['Title']))
  ```
  or alternatively via method chaining (a.k.a Fluent Interface):

  ```python
  from office365.runtime.auth.user_credential import UserCredential
  from office365.sharepoint.client_context import ClientContext
  site_url = "https://{your-tenant-prefix}.sharepoint.com"
  ctx = ClientContext(site_url).with_credentials(UserCredential("{username}", "{password}"))
  web = ctx.web.get().execute_query()
  print("Web title: {0}".format(web.properties['Title']))
  ```


  2. `RequestOptions class` - where you construct REST queries (and no model is involved)

     The example demonstrates how to read `Web` properties:

  ```python
  import json
  from office365.runtime.auth.user_credential import UserCredential
  from office365.runtime.http.request_options import RequestOptions
  from office365.sharepoint.client_context import ClientContext
  site_url = "https://{your-tenant-prefix}.sharepoint.com"
  ctx = ClientContext(site_url).with_credentials(UserCredential("{username}", "{password}"))
  request = RequestOptions("{0}/_api/web/".format(site_url))
  response = ctx.pending_request().execute_request_direct(request)
  json = json.loads(response.content)
  web_title = json['d']['Title']
  print("Web title: {0}".format(web_title))
  ```


  The list of examples:

  - Working with files
    - [download a file](examples/sharepoint/files/download_file.py)
    - [upload a file](examples/sharepoint/files/upload_file.py)

  - Working with lists and list items
    -  [create a list item](examples/sharepoint/lists/data_generator.py)
    -  [read a list item](examples/sharepoint/lists/read_large_list.py)
    -  [update a list item](examples/sharepoint/listitems/update_items_batch.py)
    -  [delete a list item](examples/sharepoint/listitems/delete_list_item.py)

  Refer [examples section](examples/sharepoint) for another scenarios



  The list of supported APIs:
  -   [Outlook Contacts REST API](https://msdn.microsoft.com/en-us/office/office365/api/contacts-rest-operations)
  -   [Outlook Calendar REST API](https://msdn.microsoft.com/en-us/office/office365/api/calendar-rest-operations)
  -   [Outlook Mail REST API](https://msdn.microsoft.com/en-us/office/office365/api/mail-rest-operations)


  Since Outlook REST APIs are available in both Microsoft Graph and the Outlook API endpoint,
  the following clients are available:

  - `GraphClient` which targets Outlook API `v2.0` version (*preferable* nowadays, refer [transition to Microsoft Graph-based Outlook REST API](https://docs.microsoft.com/en-us/outlook/rest/compare-graph-outlook) for a details)
  ~~- `OutlookClient` which targets Outlook API `v1.0` version (not recommended for usage since `v1.0` version is being deprecated.)~~




  [The Microsoft Authentication Library (MSAL) for Python](https://pypi.org/project/msal/) which comes as a dependency
  is used as a default library to obtain tokens to call Microsoft Graph API.

  Using [Microsoft Authentication Library (MSAL) for Python](https://pypi.org/project/msal/)

  > Note: access token is getting acquired via [Client Credential flow](https://docs.microsoft.com/en-us/azure/active-directory/develop/v2-oauth2-client-creds-grant-flow)
  > in the provided examples. Other forms of token aquisition can be found here: https://msal-python.readthedocs.io/en/latest/

  ```python
  import msal
  from office365.graph_client import GraphClient

  def acquire_token():
      """
      Acquire token via MSAL
      """
      authority_url = 'https://login.microsoftonline.com/{tenant_id_or_name}'
      app = msal.ConfidentialClientApplication(
          authority=authority_url,
          client_id='{client_id}',
          client_credential='{client_secret}'
      )
      token = app.acquire_token_for_client(scopes=["https://graph.microsoft.com/.default"])
      return token


  client = GraphClient(acquire_token)

  ```

  But in terms of Microsoft Graph API authentication, another OAuth spec compliant libraries
  such as [adal](https://github.com/AzureAD/azure-activedirectory-library-for-python)
  are supported as well.

  Using [ADAL Python](https://adal-python.readthedocs.io/en/latest/#)

  Usage

  ```python
  import adal
  from office365.graph_client import GraphClient

  def acquire_token_func():
      authority_url = 'https://login.microsoftonline.com/{tenant_id_or_name}'
      auth_ctx = adal.AuthenticationContext(authority_url)
      token = auth_ctx.acquire_token_with_client_credentials(
          "https://graph.microsoft.com",
          "{client_id}",
          "{client_secret}")
      return token

  client = GraphClient(acquire_token_func)

  ```



  The example demonstrates how to send an email via [Microsoft Graph endpoint](https://docs.microsoft.com/en-us/graph/api/user-sendmail?view=graph-rest-1.0&tabs=http).

  > Note: access token is getting acquired  via [Client Credential flow](https://docs.microsoft.com/en-us/azure/active-directory/develop/v2-oauth2-client-creds-grant-flow)

  ```python
  from office365.graph_client import GraphClient

  client = GraphClient(acquire_token_func)

  client.me.send_mail(
      subject="Meet for lunch?",
      body="The new cafeteria is open.",
      to_recipients=["fannyd@contoso.onmicrosoft.com"]
  ).execute_query()

  ```

  > How to enable sending emails on behalf of another user in your organization: https://learn.microsoft.com/en-us/microsoft-365/solutions/allow-members-to-send-as-or-send-on-behalf-of-group?view=o365-worldwide&viewFallbackFrom=o365-worldwide%3FWT.mc_id%3D365AdminCSH_globalsearch

  Additional examples:

  -  [download a message](examples/outlook/download_messages.py)
  -  [list messages](examples/outlook/list_message.py)
  -  [move messages to a different folder](examples/outlook/move_message.py)
  -  [search messages](examples/outlook/search_message.py)
  -  [send messages](examples/outlook/send_message.py)
  -  [send messages with attachments](examples/outlook/send_message_with_attachment.py)

  Refer to [examples section](examples/outlook) for other scenarios






  [OneDrive Graph API reference](https://docs.microsoft.com/en-us/graph/api/resources/onedrive?view=graph-rest-1.0)



  [The Microsoft Authentication Library (MSAL) for Python](https://pypi.org/project/msal/) which comes as a dependency
  is used to obtain token

  ```python
  import msal

  def acquire_token_func():
      """
      Acquire token via MSAL
      """
      authority_url = 'https://login.microsoftonline.com/{tenant_id_or_name}'
      app = msal.ConfidentialClientApplication(
          authority=authority_url,
          client_id='{client_id}',
          client_credential='{client_secret}'
      )
      token = app.acquire_token_for_client(scopes=["https://graph.microsoft.com/.default"])
      return token
  ```






  The example demonstrates how to enumerate and print drive's url
  which corresponds to [`list available drives` endpoint](https://docs.microsoft.com/en-us/onedrive/developer/rest-api/api/drive_list?view=odsp-graph-online)

  > Note: access token is getting acquired  via [Client Credential flow](https://docs.microsoft.com/en-us/azure/active-directory/develop/v2-oauth2-client-creds-grant-flow)

  ```python
  from office365.graph_client import GraphClient

  tenant_name = "contoso.onmicrosoft.com"
  client = GraphClient(acquire_token_func)
  drives = client.drives.get().execute_query()
  for drive in drives:
      print("Drive url: {0}".format(drive.web_url))
  ```




  ```python
  from office365.graph_client import GraphClient
  client = GraphClient(acquire_token_func)

  drive = client.users["{user_id_or_principal_name}"].drive.get().execute_query()

  with tempfile.TemporaryDirectory() as path:
      download_files(drive.root, path)
  ```

  where

  ```python
  def download_files(remote_folder, local_path):
      drive_items = remote_folder.children.get().execute_query()
      for drive_item in drive_items:
          if drive_item.file is not None:  # is file?
              
              with open(os.path.join(local_path, drive_item.name), 'wb') as local_file:
                  drive_item.download(local_file).execute_query()
  ```

  Additional examples:

  -  [create list column](examples/onedrive/create_list_column.py)
  -  [download file](examples/onedrive/download_file_default.py)
  -  [export files](examples/onedrive/export_files.py)
  -  [import files](examples/onedrive/import_files.py)
  -  [list drives](examples/onedrive/list_drives.py)
  -  [list files](examples/onedrive/list_files.py)

  Refer to [OneDrive examples section](examples/onedrive) for more examples.






  [The Microsoft Authentication Library (MSAL) for Python](https://pypi.org/project/msal/) which comes as a dependency
  is used to obtain token





  The example demonstrates how create a new team under a group
  which corresponds to [`Create team` endpoint](https://docs.microsoft.com/en-us/graph/api/team-put-teams?view=graph-rest-1.0&tabs=http)

  ```python
  from office365.graph_client import GraphClient
  client = GraphClient(acquire_token_func)
  new_team = client.groups["{group_id}"].add_team().execute_query_retry()
  ```

  Additional examples:

  -  [create a team](examples/teams/create_team.py)
  -  [create team from group](examples/teams/create_team_from_group.py)
  -  [list all teams](examples/teams/list_all_teams.py)
  -  [list my teams](examples/teams/list_my_teams.py)
  -  [send messages](examples/teams/send_message.py)

  Refer to [examples section](examples/teams) for other scenarios



  The library supports OneNote API in terms of calls to a user's OneNote notebooks, sections, and pages in a personal or organization account

  Example: Create a new page

  ```python
  from office365.graph_client import GraphClient
  client = GraphClient(acquire_token_func)

  files = {}
  with open("./MyPage.html", 'rb') as f, \
      open("./MyImage.png", 'rb') as img_f, \
      open("./MyDoc.pdf", 'rb') as pdf_f:
      files["imageBlock1"] = img_f
      files["fileBlock1"] = pdf_f
      page = client.me.onenote.pages.add(presentation_file=f, attachment_files=files).execute_query()
  ```



  The example demonstrates how to create a new planner task
  which corresponds to [`Create plannerTask` endpoint](https://docs.microsoft.com/en-us/graph/api/planner-post-tasks?view=graph-rest-1.0):

  ```python
  from office365.graph_client import GraphClient

  client = GraphClient(acquire_token_func)
  task = client.planner.tasks.add(title="New task", planId="--plan id goes here--").execute_query()
  ```


  The following libraries will be installed when you install the client library:
  * [requests](https://github.com/kennethreitz/requests)
  * [Microsoft Authentication Library (MSAL) for Python](https://pypi.org/project/msal/)

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pkgId: 79d596adb8afe23e5ef69dc63598e25ae844899b3d99b29210d22cb5687ade40
name: python3-a-pytube-fork-for-spotdl-users
arch: noarch
version: 9.6.4
epoch: 0
release: 1
summary: Python 3 library for downloading YouTube Videos.
url: https://github.com/nficano/pytube
rpm_license: MIT
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rpm_buildhost: resalloc-87e8b7a1-55c8-4811-922a-8bf458a1646e
rpm_sourcerpm: python-a-pytube-fork-for-spotdl-users-9.6.4-1.src.rpm
checksum_type: sha256
description:
  Ping @ronncc if you would like to help out

  <div align="center">
    <p>
    <img src="https://github.com/nficano/pytube/blob/master/images/pytube.png?raw=true" width="350" height="328" alt="pytube logo" />
    </p>
    <p align="center">
  	  <img src="https://img.shields.io/pypi/v/pytube.svg" alt="pypi">
  	  <a href="https://travis-ci.org/nficano/pytube"><img src="https://travis-ci.org/nficano/pytube.svg?branch=master" /></a>
  	  <a href="http://python-pytube.readthedocs.io/en/latest/?badge=latest"><img src="https://readthedocs.org/projects/python-pytube/badge/?version=latest" /></a>
  	  <a href="https://coveralls.io/github/nficano/pytube?branch=master"><img src="https://coveralls.io/repos/github/nficano/pytube/badge.svg?branch=master#23e6f7ac56dd3bde" /></a>
        <a href="https://pypi.org/project/pytube/"><img src="https://img.shields.io/pypi/dm/pytube.svg" alt="pypi"></a>
  	  <a href="https://pypi.python.org/pypi/pytube/"><img src="https://img.shields.io/pypi/pyversions/pytube.svg" /></a>
      <p>
    </p>
  </div>


  *pytube* is a very serious, lightweight, dependency-free Python library (and command-line utility) for downloading YouTube Videos.


  YouTube is the most popular video-sharing platform in the world and as a hacker you may encounter a situation where you want to script something to download videos.  For this I present to you *pytube*.

  *pytube* is a lightweight library written in Python. It has no third party dependencies and aims to be highly reliable.

  *pytube* also makes pipelining easy, allowing you to specify callback functions for different download events, such as  ``on progress`` or ``on complete``.

  Finally *pytube* also includes a command-line utility, allowing you to quickly download videos right from terminal.



  ```python
   >>> YouTube('https://youtu.be/9bZkp7q19f0').streams.first().download()
   >>> yt = YouTube('http://youtube.com/watch?v=9bZkp7q19f0')
   >>> yt.streams
    ... .filter(progressive=True, file_extension='mp4')
    ... .order_by('resolution')
    ... .desc()
    ... .first()
    ... .download()
  ```


  - Support for Both Progressive & DASH Streams
  - Support for downloading complete playlist
  - Easily Register ``on_download_progress`` & ``on_download_complete`` callbacks
  - Command-line Interfaced Included
  - Caption Track Support
  - Outputs Caption Tracks to .srt format (SubRip Subtitle)
  - Ability to Capture Thumbnail URL.
  - Extensively Documented Source Code
  - No Third-Party Dependencies



  Download using pip via pypi.

  ```bash
  $ pip install pytube
  ```



  Let's begin with showing how easy it is to download a video with pytube:

  ```python
  >>> from pytube import YouTube
  >>> YouTube('http://youtube.com/watch?v=9bZkp7q19f0').streams.first().download()
  ```
  This example will download the highest quality progressive download stream available.

  Next, let's explore how we would view what video streams are available:

  ```python
  >>> yt = YouTube('http://youtube.com/watch?v=9bZkp7q19f0')
  >>> yt.streams.all()
   [<Stream: itag="22" mime_type="video/mp4" res="720p" fps="30fps" vcodec="avc1.64001F" acodec="mp4a.40.2">,
   <Stream: itag="43" mime_type="video/webm" res="360p" fps="30fps" vcodec="vp8.0" acodec="vorbis">,
   <Stream: itag="18" mime_type="video/mp4" res="360p" fps="30fps" vcodec="avc1.42001E" acodec="mp4a.40.2">,
   <Stream: itag="36" mime_type="video/3gpp" res="240p" fps="30fps" vcodec="mp4v.20.3" acodec="mp4a.40.2">,
   <Stream: itag="17" mime_type="video/3gpp" res="144p" fps="30fps" vcodec="mp4v.20.3" acodec="mp4a.40.2">,
   <Stream: itag="137" mime_type="video/mp4" res="1080p" fps="30fps" vcodec="avc1.640028">,
   <Stream: itag="248" mime_type="video/webm" res="1080p" fps="30fps" vcodec="vp9">,
   <Stream: itag="136" mime_type="video/mp4" res="720p" fps="30fps" vcodec="avc1.4d401f">,
   <Stream: itag="247" mime_type="video/webm" res="720p" fps="30fps" vcodec="vp9">,
   <Stream: itag="135" mime_type="video/mp4" res="480p" fps="30fps" vcodec="avc1.4d401e">,
   <Stream: itag="244" mime_type="video/webm" res="480p" fps="30fps" vcodec="vp9">,
   <Stream: itag="134" mime_type="video/mp4" res="360p" fps="30fps" vcodec="avc1.4d401e">,
   <Stream: itag="243" mime_type="video/webm" res="360p" fps="30fps" vcodec="vp9">,
   <Stream: itag="133" mime_type="video/mp4" res="240p" fps="30fps" vcodec="avc1.4d4015">,
   <Stream: itag="242" mime_type="video/webm" res="240p" fps="30fps" vcodec="vp9">,
   <Stream: itag="160" mime_type="video/mp4" res="144p" fps="30fps" vcodec="avc1.4d400c">,
   <Stream: itag="278" mime_type="video/webm" res="144p" fps="30fps" vcodec="vp9">,
   <Stream: itag="140" mime_type="audio/mp4" abr="128kbps" acodec="mp4a.40.2">,
   <Stream: itag="171" mime_type="audio/webm" abr="128kbps" acodec="vorbis">,
   <Stream: itag="249" mime_type="audio/webm" abr="50kbps" acodec="opus">,
   <Stream: itag="250" mime_type="audio/webm" abr="70kbps" acodec="opus">,
   <Stream: itag="251" mime_type="audio/webm" abr="160kbps" acodec="opus">]
  ```
  You may notice that some streams listed have both a video codec and audio codec, while others have just video or just audio, this is a result of YouTube supporting a streaming technique called Dynamic Adaptive Streaming over HTTP (DASH).

  In the context of pytube, the implications are for the highest quality streams; you now need to download both the audio and video tracks and then post-process them with software like FFmpeg to merge them.

  The legacy streams that contain the audio and video in a single file (referred to as "progressive download") are still available, but only for resolutions 720p and below.

  To only view these progressive download streams:

  ```python
   >>> yt.streams.filter(progressive=True).all()
    [<Stream: itag="22" mime_type="video/mp4" res="720p" fps="30fps" vcodec="avc1.64001F" acodec="mp4a.40.2">,
    <Stream: itag="43" mime_type="video/webm" res="360p" fps="30fps" vcodec="vp8.0" acodec="vorbis">,
    <Stream: itag="18" mime_type="video/mp4" res="360p" fps="30fps" vcodec="avc1.42001E" acodec="mp4a.40.2">,
    <Stream: itag="36" mime_type="video/3gpp" res="240p" fps="30fps" vcodec="mp4v.20.3" acodec="mp4a.40.2">,
    <Stream: itag="17" mime_type="video/3gpp" res="144p" fps="30fps" vcodec="mp4v.20.3" acodec="mp4a.40.2">]
  ```

  Conversely, if you only want to see the DASH streams (also referred to as "adaptive") you can do:

  ```python
  >>> yt.streams.filter(adaptive=True).all()
   [<Stream: itag="137" mime_type="video/mp4" res="1080p" fps="30fps" vcodec="avc1.640028">,
    <Stream: itag="248" mime_type="video/webm" res="1080p" fps="30fps" vcodec="vp9">,
    <Stream: itag="136" mime_type="video/mp4" res="720p" fps="30fps" vcodec="avc1.4d401f">,
    <Stream: itag="247" mime_type="video/webm" res="720p" fps="30fps" vcodec="vp9">,
    <Stream: itag="135" mime_type="video/mp4" res="480p" fps="30fps" vcodec="avc1.4d401e">,
    <Stream: itag="244" mime_type="video/webm" res="480p" fps="30fps" vcodec="vp9">,
    <Stream: itag="134" mime_type="video/mp4" res="360p" fps="30fps" vcodec="avc1.4d401e">,
    <Stream: itag="243" mime_type="video/webm" res="360p" fps="30fps" vcodec="vp9">,
    <Stream: itag="133" mime_type="video/mp4" res="240p" fps="30fps" vcodec="avc1.4d4015">,
    <Stream: itag="242" mime_type="video/webm" res="240p" fps="30fps" vcodec="vp9">,
    <Stream: itag="160" mime_type="video/mp4" res="144p" fps="30fps" vcodec="avc1.4d400c">,
    <Stream: itag="278" mime_type="video/webm" res="144p" fps="30fps" vcodec="vp9">,
    <Stream: itag="140" mime_type="audio/mp4" abr="128kbps" acodec="mp4a.40.2">,
    <Stream: itag="171" mime_type="audio/webm" abr="128kbps" acodec="vorbis">,
    <Stream: itag="249" mime_type="audio/webm" abr="50kbps" acodec="opus">,
    <Stream: itag="250" mime_type="audio/webm" abr="70kbps" acodec="opus">,
    <Stream: itag="251" mime_type="audio/webm" abr="160kbps" acodec="opus">]
  ```

  You can also download a complete Youtube playlist:

  ```python
  >>> from pytube import Playlist
  >>> pl = Playlist("https://www.youtube.com/watch?v=Edpy1szoG80&list=PL153hDY-y1E00uQtCVCVC8xJ25TYX8yPU")
  >>> pl.download_all()
  >>> # or if you want to download in a specific directory
  >>> pl.download_all('/path/to/directory/')
  ```
  This will download the highest progressive stream available (generally 720p) from the given playlist. Later more options would be given for user's flexibility
  to choose video resolution.

  Pytube allows you to filter on every property available (see the documentation for the complete list), let's take a look at some of the most useful ones.

  To list the audio only streams:

  ```python
  >>> yt.streams.filter(only_audio=True).all()
    [<Stream: itag="140" mime_type="audio/mp4" abr="128kbps" acodec="mp4a.40.2">,
    <Stream: itag="171" mime_type="audio/webm" abr="128kbps" acodec="vorbis">,
    <Stream: itag="249" mime_type="audio/webm" abr="50kbps" acodec="opus">,
    <Stream: itag="250" mime_type="audio/webm" abr="70kbps" acodec="opus">,
    <Stream: itag="251" mime_type="audio/webm" abr="160kbps" acodec="opus">]
  ```

  To list only ``mp4`` streams:

  ```python
  >>> yt.streams.filter(subtype='mp4').all()
   [<Stream: itag="22" mime_type="video/mp4" res="720p" fps="30fps" vcodec="avc1.64001F" acodec="mp4a.40.2">,
    <Stream: itag="18" mime_type="video/mp4" res="360p" fps="30fps" vcodec="avc1.42001E" acodec="mp4a.40.2">,
    <Stream: itag="137" mime_type="video/mp4" res="1080p" fps="30fps" vcodec="avc1.640028">,
    <Stream: itag="136" mime_type="video/mp4" res="720p" fps="30fps" vcodec="avc1.4d401f">,
    <Stream: itag="135" mime_type="video/mp4" res="480p" fps="30fps" vcodec="avc1.4d401e">,
    <Stream: itag="134" mime_type="video/mp4" res="360p" fps="30fps" vcodec="avc1.4d401e">,
    <Stream: itag="133" mime_type="video/mp4" res="240p" fps="30fps" vcodec="avc1.4d4015">,
    <Stream: itag="160" mime_type="video/mp4" res="144p" fps="30fps" vcodec="avc1.4d400c">,
    <Stream: itag="140" mime_type="audio/mp4" abr="128kbps" acodec="mp4a.40.2">]
  ```

  Multiple filters can also be specified:

  ```python
  >>> yt.streams.filter(subtype='mp4', progressive=True).all()
  >>> # this can also be expressed as:
  >>> yt.streams.filter(subtype='mp4').filter(progressive=True).all()
    [<Stream: itag="22" mime_type="video/mp4" res="720p" fps="30fps" vcodec="avc1.64001F" acodec="mp4a.40.2">,
    <Stream: itag="18" mime_type="video/mp4" res="360p" fps="30fps" vcodec="avc1.42001E" acodec="mp4a.40.2">]
  ```
  You also have an interface to select streams by their itag, without needing to filter:

  ```python
  >>> yt.streams.get_by_itag(22)
    <Stream: itag="22" mime_type="video/mp4" res="720p" fps="30fps" vcodec="avc1.64001F" acodec="mp4a.40.2">
  ```

  If you need to optimize for a specific feature, such as the "highest resolution" or "lowest average bitrate":

  ```python
  >>> yt.streams.filter(progressive=True).order_by('resolution').desc().all()
  ```
  Note that ``order_by`` cannot be used if your attribute is undefined in any of the Stream instances, so be sure to apply a filter to remove those before calling it.

  If your application requires post-processing logic, pytube allows you to specify an "on download complete" callback function:

  ```python
   >>> def convert_to_aac(stream, file_handle):
           return  # do work

   >>> yt.register_on_complete_callback(convert_to_aac)
  ```

  Similarly, if your application requires on-download progress logic, pytube exposes a callback for this as well:

  ```python
   >>> def show_progress_bar(stream, chunk, file_handle, bytes_remaining):
           return  # do work

   >>> yt.register_on_progress_callback(show_progress_bar)
  ```



  pytube also ships with a tiny cli interface for downloading and probing videos.

  Let's start with downloading:

  ```bash
  $ pytube http://youtube.com/watch?v=9bZkp7q19f0 --itag=22
  ```
  To view available streams:

  ```bash
  $ pytube http://youtube.com/watch?v=9bZkp7q19f0 --list
  ```

  Finally, if you're filing a bug report, the cli contains a switch called ``--build-playback-report``, which bundles up the state, allowing others to easily replay your issue.

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  This is a provider package for ``sftp`` provider. All classes for this provider package
  are in ``airflow.providers.sftp`` python package.
  You can find package information and changelog for the provider
  in the `documentation <https://airflow.apache.org/docs/apache-airflow-providers-sftp/4.2.4/>`_.

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  You can find package information and changelog for the provider
  in the `documentation <https://airflow.apache.org/docs/apache-airflow-providers-snowflake/4.0.4/>`_.

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  are in ``airflow.providers.sqlite`` python package.
  You can find package information and changelog for the provider
  in the `documentation <https://airflow.apache.org/docs/apache-airflow-providers-sqlite/3.3.1/>`_.

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description:
  This is a provider package for ``ssh`` provider. All classes for this provider package
  are in ``airflow.providers.ssh`` python package.
  You can find package information and changelog for the provider
  in the `documentation <https://airflow.apache.org/docs/apache-airflow-providers-ssh/3.6.0/>`_.

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summary: Provider for Apache Airflow. Implements apache-airflow-providers-tableau package
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description:
  This is a provider package for ``tableau`` provider. All classes for this provider package
  are in ``airflow.providers.tableau`` python package.
  You can find package information and changelog for the provider
  in the `documentation <https://airflow.apache.org/docs/apache-airflow-providers-tableau/4.1.0/>`_.

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  This package is not intended to be installed directly by the end user.
  It provides the necessary files for other packages to extend the azure cli command module namespaces.

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pkgId: baf28f9e95751fcbe2e9c161764c32d0bf23d33756394b04b9b3bbd0215b0cd0
name: python3-azure-eventhub-checkpointstoreblob
arch: noarch
version: 1.1.4
epoch: 0
release: 1
summary: Microsoft Azure Event Hubs checkpointer implementation with Blob Storage Client Library for Python
rpm_license: MIT License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-5383eccd-d760-4086-8cea-633089fddb22
rpm_sourcerpm: python-azure-eventhub-checkpointstoreblob-1.1.4-1.src.rpm
checksum_type: sha256
description:
  Azure EventHubs Checkpoint Store is used for storing checkpoints while processing events from Azure Event Hubs.
  This Checkpoint Store package works as a plug-in package to `EventHubConsumerClient`. It uses Azure Storage Blob as the persistent store for maintaining checkpoints and partition ownership information.

  Please note that this is a sync library, for async version of the Azure EventHubs Checkpoint Store client library, please refer to [azure-eventhub-checkpointstoreblob-aio](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub-checkpointstoreblob-aio).

  [Source code](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub-checkpointstoreblob/) | [Package (PyPi)](https://pypi.org/project/azure-eventhub-checkpointstoreblob) | [API reference documentation](https://azuresdkdocs.blob.core.windows.net/$web/python/azure-eventhub/latest/azure.eventhub.html#azure.eventhub.CheckpointStore) | [Azure Eventhubs documentation](https://docs.microsoft.com/azure/event-hubs/) | [Azure Storage documentation](https://docs.microsoft.com/azure/storage/)





  - Python2.7, Python 3.6 or later.
  - **Microsoft Azure Subscription:**  To use Azure services, including Azure Event Hubs, you'll need a subscription. If you do not have an existing Azure account, you may sign up for a free trial or use your MSDN subscriber benefits when you [create an account](https://azure.microsoft.com/).

  - **Event Hubs namespace with an Event Hub:** To interact with Azure Event Hubs, you'll also need to have a namespace and Event Hub  available.  If you are not familiar with creating Azure resources, you may wish to follow the step-by-step guide for [creating an Event Hub using the Azure portal](https://docs.microsoft.com/azure/event-hubs/event-hubs-create).  There, you can also find detailed instructions for using the Azure CLI, Azure PowerShell, or Azure Resource Manager (ARM) templates to create an Event Hub.

  - **Azure Storage Account:** You'll need to have an Azure Storage Account and create a Azure Blob Storage Block Container to store the checkpoint data with blobs. You may follow the guide [creating an Azure Block Blob Storage Account](https://docs.microsoft.com/azure/storage/blobs/storage-blob-create-account-block-blob).



  ```
  $ pip install azure-eventhub-checkpointstoreblob
  ```





  Checkpointing is a process by which readers mark or commit their position within a partition event sequence.
  Checkpointing is the responsibility of the consumer and occurs on a per-partition basis within a consumer group.
  This responsibility means that for each consumer group, each partition reader must keep track of its current position
  in the event stream, and can inform the service when it considers the data stream complete. If a reader disconnects from
  a partition, when it reconnects it begins reading at the checkpoint that was previously submitted by the last reader of
  that partition in that consumer group. When the reader connects, it passes the offset to the event hub to specify the
  location at which to start reading. In this way, you can use checkpointing to both mark events as "complete" by
  downstream applications, and to provide resiliency if a failover between readers running on different machines occurs.
  It is possible to return to older data by specifying a lower offset from this checkpointing process. Through this
  mechanism, checkpointing enables both failover resiliency and event stream replay.


  Both offset & sequence number refer to the position of an event within a partition. You can think of them as a
  client-side cursor. The offset is a byte numbering of the event. The offset/sequence number enables an event consumer
  (reader) to specify a point in the event stream from which they want to begin reading events. You can specify a
  timestamp such that you receive events enqueued only after the given timestamp. Consumers are responsible for
  storing their own offset values outside of the Event Hubs service. Within a partition, each event includes an offset,
  sequence number and the timestamp of when it was enqueued.


  - [Create an Azure EventHubs `EventHubConsumerClient`](#create-an-eventhubconsumerclient)
  - [Consume events using a `BlobCheckpointStore`](#consume-events-using-a-blobcheckpointstore-to-do-checkpoint)


  The easiest way to create a `EventHubConsumerClient` is to use a connection string.
  ```python
  from azure.eventhub import EventHubConsumerClient
  eventhub_client = EventHubConsumerClient.from_connection_string("my_eventhub_namespace_connection_string", "my_consumer_group", eventhub_name="my_eventhub")
  ```
  For other ways of creating a `EventHubConsumerClient`, refer to [EventHubs library](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub) for more details.


  ```python

  from azure.eventhub import EventHubConsumerClient
  from azure.eventhub.extensions.checkpointstoreblob import BlobCheckpointStore

  connection_str = '<< CONNECTION STRING FOR THE EVENT HUBS NAMESPACE >>'
  consumer_group = '<< CONSUMER GROUP >>'
  eventhub_name = '<< NAME OF THE EVENT HUB >>'
  storage_connection_str = '<< CONNECTION STRING OF THE STORAGE >>'
  container_name = '<< STORAGE CONTAINER NAME>>'


  def on_event(partition_context, event):
      
      partition_context.update_checkpoint(event)  # Or update_checkpoint every N events for better performance.

  def main():
      checkpoint_store = BlobCheckpointStore.from_connection_string(
          storage_connection_str,
          container_name
      )
      client = EventHubConsumerClient.from_connection_string(
          connection_str,
          consumer_group,
          eventhub_name=eventhub_name,
          checkpoint_store=checkpoint_store,
      )

      with client:
          client.receive(on_event)

  if __name__ == '__main__':
      main()
  ```


  Some environments have different versions of Azure Storage Service API.
  `BlobCheckpointStore` by default uses the Storage Service API version 2019-07-07. To use it against a different
  version, specify `api_version` when you create the `BlobCheckpointStore` object.





  Enabling logging will be helpful to do trouble shooting.



  - Enable `azure.eventhub.extensions.checkpointstoreblob` logger to collect traces from the library.
  - Enable `azure.eventhub` logger to collect traces from the main azure-eventhub library.
  - Enable `azure.eventhub.extensions.checkpointstoreblob._vendor.storage` logger to collect traces from azure storage blob library.
  - Enable `uamqp` logger to collect traces from the underlying uAMQP library.
  - Enable AMQP frame level trace by setting `logging_enable=True` when creating the client.





  Get started with our [EventHubs Checkpoint Store samples](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub-checkpointstoreblob/samples).

  - [receive_events_using_checkpoint_store.py](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub-checkpointstoreblob/samples/receive_events_using_checkpoint_store.py) - EventHubConsumerClient with blob checkpoint store example
  - [receive_events_using_checkpoint_store_storage_api_version.py](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub-checkpointstoreblob/samples/receive_events_using_checkpoint_store_storage_api_version.py) - EventHubConsumerClient with blob checkpoint store and storage version example



  Reference documentation is available [here](https://azuresdkdocs.blob.core.windows.net/$web/python/azure-eventhub/latest/azure.eventhub.html#azure.eventhub.CheckpointStore).



  If you encounter any bugs or have suggestions, please file an issue in the [Issues](https://github.com/Azure/azure-sdk-for-python/issues) section of the project.



  This project welcomes contributions and suggestions.  Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

  When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

  This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
  For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

  ![Impressions](https://azure-sdk-impressions.azurewebsites.net/api/impressions/azure-sdk-for-python/sdk/eventhub/azure-eventhub-checkpointstoreblob/README.png)






  This version and all future versions will require Python 2.7 or Python 3.6+, Python 3.5 is no longer supported.

  **New features**
  - Updated `list_ownership`, `claim_ownership`, `update_checkpoint`, `list_checkpoints` on `BlobCheckpointStore` to support taking `**kwargs`.



  This version will be the last version to officially support Python 3.5, future versions will require Python 2.7 or Python 3.6+.

  **Bug fixes**
  - Updated vendor azure-storage-blob dependency to v12.7.1.
    - Fixed storage blob authentication failure due to request date header too old (#16192).



  **Bug fixes**
  - Fixed a bug that `BlobCheckpointStore.list_ownership` and `BlobCheckpointStore.list_checkpoints` triggering `KeyError` due to reading empty metadata of parent node when working with Data Lake enabled Blob Storage.



  **Bug fixes**
  - Fixed a bug that may gradually slow down retrieving checkpoint data from the storage blob if the storage account "File share soft delete" is enabled. #12836



  **New features**
  - Param `api_version` of `BlobCheckpointStore` now supports older versions of Azure Storage Service API.


  Stable release. No new features or API changes.



  **Breaking changes**

  - Renamed `BlobPartitionManager` to `BlobCheckpointStore`.
  - Constructor of `BlobCheckpointStore` has been updated to take the storage container details directly rather than an instance of `ContainerClient`.
  - A `from_connection_string` constructor has been added for Blob Storage connection strings.
  - Module `blobstoragepm` is now internal, all imports should be directly from `azure.eventhub.extensions.checkpointstoreblob`.
  - `BlobCheckpointStore` now has a `close()` function for shutting down an HTTP connection pool, additionally the object can be used in a context manager to manage the connection.



  **New features**

  - `BlobPartitionManager` that uses Azure Blob Storage Block Blob to store EventProcessor checkpoint data

  ![Impressions](https://azure-sdk-impressions.azurewebsites.net/api/impressions/azure-sdk-for-python/sdk/eventhub/azure-eventhub-checkpointstoreblob/HISTORY.png)

url:
  https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/eventhub/azure-eventhub-checkpointstoreblob

location_href:
  00010620-python-azure-eventhub-checkpointstoreblob/python3-azure-eventhub-checkpointstoreblob-1.1.4-1.noarch.rpm


pkgId: 1716322bc823c875531371c71ee6808deea28e43edceeda58937447d79e0323e
name: python3-azure-eventhub-checkpointstoreblob-aio
arch: noarch
version: 1.1.4
epoch: 0
release: 1
summary: Microsoft Azure Event Hubs checkpointer implementation with Blob Storage Client Library for Python
rpm_license: MIT License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-1a132514-106f-4be4-909c-0233bc6a1e93
rpm_sourcerpm: python-azure-eventhub-checkpointstoreblob-aio-1.1.4-1.src.rpm
checksum_type: sha256
description:
  Azure EventHubs Checkpoint Store is used for storing checkpoints while processing events from Azure Event Hubs.
  This Checkpoint Store package works as a plug-in package to `EventHubConsumerClient`. It uses Azure Storage Blob as the persistent store for maintaining checkpoints and partition ownership information.

  Please note that this is an async library, for sync version of the Azure EventHubs Checkpoint Store client library, please refer to [azure-eventhub-checkpointstoreblob](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub-checkpointstoreblob).

  [Source code](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub-checkpointstoreblob-aio/) | [Package (PyPi)](https://pypi.org/project/azure-eventhub-checkpointstoreblob-aio/) | [API reference documentation](https://azuresdkdocs.blob.core.windows.net/$web/python/azure-eventhub/latest/azure.eventhub.aio.html#azure.eventhub.aio.CheckpointStore) | [Azure Eventhubs documentation](https://docs.microsoft.com/azure/event-hubs/) | [Azure Storage documentation](https://docs.microsoft.com/azure/storage/)





  - Python 3.6 or later.
  - **Microsoft Azure Subscription:**  To use Azure services, including Azure Event Hubs, you'll need a subscription.  If you do not have an existing Azure account, you may sign up for a free trial or use your MSDN subscriber benefits when you [create an account](https://azure.microsoft.com/).

  - **Event Hubs namespace with an Event Hub:** To interact with Azure Event Hubs, you'll also need to have a namespace and Event Hub  available.  If you are not familiar with creating Azure resources, you may wish to follow the step-by-step guide for [creating an Event Hub using the Azure portal](https://docs.microsoft.com/azure/event-hubs/event-hubs-create).  There, you can also find detailed instructions for using the Azure CLI, Azure PowerShell, or Azure Resource Manager (ARM) templates to create an Event Hub.

  - **Azure Storage Account:** You'll need to have an Azure Storage Account and create a Azure Blob Storage Block Container to store the checkpoint data with blobs. You may follow the guide [creating an Azure Block Blob Storage Account](https://docs.microsoft.com/azure/storage/blobs/storage-blob-create-account-block-blob).



  ```
  $ pip install azure-eventhub-checkpointstoreblob-aio
  ```





  Checkpointing is a process by which readers mark or commit their position within a partition event sequence.
  Checkpointing is the responsibility of the consumer and occurs on a per-partition basis within a consumer group.
  This responsibility means that for each consumer group, each partition reader must keep track of its current position
  in the event stream, and can inform the service when it considers the data stream complete. If a reader disconnects from
  a partition, when it reconnects it begins reading at the checkpoint that was previously submitted by the last reader of
  that partition in that consumer group. When the reader connects, it passes the offset to the event hub to specify the
  location at which to start reading. In this way, you can use checkpointing to both mark events as "complete" by
  downstream applications, and to provide resiliency if a failover between readers running on different machines occurs.
  It is possible to return to older data by specifying a lower offset from this checkpointing process. Through this
  mechanism, checkpointing enables both failover resiliency and event stream replay.


  Both offset & sequence number refer to the position of an event within a partition. You can think of them as a
  client-side cursor. The offset is a byte numbering of the event. The offset/sequence number enables an event consumer
  (reader) to specify a point in the event stream from which they want to begin reading events. You can specify a
  timestamp such that you receive events enqueued only after the given timestamp. Consumers are responsible for
  storing their own offset values outside of the Event Hubs service. Within a partition, each event includes an offset,
  sequence number and the timestamp of when it was enqueued.


  - [Create an Azure EventHubs `EventHubConsumerClient`](#create-an-eventhubconsumerclient)
  - [Consume events using a `BlobCheckpointStore`](#consume-events-using-a-blobcheckpointstore-to-do-checkpoint)


  The easiest way to create a `EventHubConsumerClient` is to use a connection string.
  ```python
  from azure.eventhub.aio import EventHubConsumerClient
  eventhub_client = EventHubConsumerClient.from_connection_string("my_eventhub_namespace_connection_string", "my_consumer_group", eventhub_name="my_eventhub")
  ```
  For other ways of creating a `EventHubConsumerClient`, refer to [EventHubs library](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub) for more details.


  ```python
  import asyncio

  from azure.eventhub.aio import EventHubConsumerClient
  from azure.eventhub.extensions.checkpointstoreblobaio import BlobCheckpointStore

  connection_str = '<< CONNECTION STRING FOR THE EVENT HUBS NAMESPACE >>'
  consumer_group = '<< CONSUMER GROUP >>'
  eventhub_name = '<< NAME OF THE EVENT HUB >>'
  storage_connection_str = '<< CONNECTION STRING OF THE STORAGE >>'
  container_name = '<< STORAGE CONTAINER NAME>>'

  async def on_event(partition_context, event):
      
      await partition_context.update_checkpoint(event)  # Or update_checkpoint every N events for better performance.

  async def main():
      checkpoint_store = BlobCheckpointStore.from_connection_string(
          storage_connection_str,
          container_name
      )
      client = EventHubConsumerClient.from_connection_string(
          connection_str,
          consumer_group,
          eventhub_name=eventhub_name,
          checkpoint_store=checkpoint_store,
      )

      async with client:
          await client.receive(on_event)

  if __name__ == '__main__':
      loop = asyncio.get_event_loop()
      loop.run_until_complete(main())
  ```


  Some environments have different versions of Azure Storage Service API.
  `BlobCheckpointStore` by default uses the Storage Service API version 2019-07-07. To use it against a different
  version, specify `api_version` when you create the `BlobCheckpointStore` object.





  Enabling logging will be helpful to do trouble shooting.



  - Enable `azure.eventhub.extensions.checkpointstoreblobaio` logger to collect traces from the library.
  - Enable `azure.eventhub` logger to collect traces from the main azure-eventhub library.
  - Enable `azure.eventhub.extensions.checkpointstoreblobaio._vendor.storage` logger to collect traces from azure storage blob library.
  - Enable `uamqp` logger to collect traces from the underlying uAMQP library.
  - Enable AMQP frame level trace by setting `logging_enable=True` when creating the client.





  Get started with our [EventHubs Checkpoint Store async samples](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub-checkpointstoreblob-aio/samples).

  - [receive_events_using_checkpoint_store_async.py](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub-checkpointstoreblob-aio/samples/receive_events_using_checkpoint_store_async.py) - EventHubConsumerClient with blob checkpoint store example
  - [receive_events_using_checkpoint_store_storage_api_version_async.py](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/eventhub/azure-eventhub-checkpointstoreblob-aio/samples/receive_events_using_checkpoint_store_storage_api_version_async.py) - EventHubConsumerClient with blob checkpoint store and storage version example



  Reference documentation is available [here](https://azuresdkdocs.blob.core.windows.net/$web/python/azure-eventhub/latest/azure.eventhub.aio.html#azure.eventhub.aio.CheckpointStore).



  If you encounter any bugs or have suggestions, please file an issue in the [Issues](https://github.com/Azure/azure-sdk-for-python/issues) section of the project.



  This project welcomes contributions and suggestions.  Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

  When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

  This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
  For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

  ![Impressions](https://azure-sdk-impressions.azurewebsites.net/api/impressions/azure-sdk-for-python/sdk/eventhub/azure-eventhub-checkpointstoreblob-aio/README.png)






  This version and all future versions will require Python 2.7 or Python 3.6+, Python 3.5 is no longer supported.

  **New features**
  - Updated `list_ownership`, `claim_ownership`, `update_checkpoint`, `list_checkpoints` on `BlobCheckpointStore` to support taking `**kwargs`.



  This version will be the last version to officially support Python 3.5, future versions will require Python 2.7 or Python 3.6+.

  **Bug fixes**
  - Updated vendor azure-storage-blob dependency to v12.7.1.
    - Fixed storage blob authentication failure due to request date header too old (#16192).




  **Bug fixes**
  - Fixed a bug that `BlobCheckpointStore.list_ownership` and `BlobCheckpointStore.list_checkpoints` triggering `KeyError` due to reading empty metadata of parent node when working with Data Lake enabled Blob Storage.



  **Bug fixes**
  - Fixed a bug that may gradually slow down retrieving checkpoint data from the storage blob if the storage account "File share soft delete" is enabled. #12836



  **New features**
  - Param `api_version` of `BlobCheckpointStore` now supports older versions of Azure Storage Service API.


  Stable release. No new features or API changes.



  **Breaking changes**

  - Renamed `BlobPartitionManager` to `BlobCheckpointStore`.
  - Constructor of `BlobCheckpointStore` has been updated to take the storage container details directly rather than an instance of `ContainerClient`.
  - A `from_connection_string` constructor has been added for Blob Storage connection strings.
  - Module `blobstoragepmaio` is now internal, all imports should be directly from `azure.eventhub.extensions.checkpointstoreblobaio`.
  - `BlobCheckpointStore` now has a `close()` function for shutting down an HTTP connection pool, additionally the object can be used in a context manager to manage the connection.



  **New features**

  - Added method `list_checkpoints` which list all the checkpoints under given eventhub namespace, eventhub name and consumer group.


  This release has trivial internal changes only. No feature changes.



  **New features**

  - `BlobPartitionManager` that uses Azure Blob Storage Block Blob to store EventProcessor checkpoint data

  ![Impressions](https://azure-sdk-impressions.azurewebsites.net/api/impressions/azure-sdk-for-python/sdk/eventhub/azure-eventhub-checkpointstoreblob-aio/HISTORY.png)

url:
  https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/eventhub/azure-eventhub-checkpointstoreblob-aio

location_href:
  00010354-python-azure-eventhub-checkpointstoreblob-aio/python3-azure-eventhub-checkpointstoreblob-aio-1.1.4-1.noarch.rpm


pkgId: 9b1d746c82502fa417d80a80d727aecc7745be751995b1ad67edede04a0fcbef
name: python3-azure-functions-devops-build
arch: noarch
version: 0.0.22
epoch: 0
release: 1
url: https://github.com/Azure/azure-functions-devops-build
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-037e16f4-9d88-4ef6-b198-03ea52cfb450
rpm_sourcerpm: python-azure-functions-devops-build-0.0.22-1.src.rpm
checksum_type: sha256
summary:
  Python package for integrating Azure Functions with Azure DevOps. Specifically made for the Azure CLI

description:
  > :construction: The project is currently **work in progress**. Please **do not use in production** as we expect developments over time. :construction:

  This project provides the class AzureDevopsBuildManager and supporting classes. This manager class allows
  the caller to manage Azure Devops pipelines that are maintained within an Azure Devops account. This project was created to be able to support command line tooling for the AZ Cli.


  To install the package from pip:
  ```
  pip install azure-functions-devops-build
  ```

  To use the API, you need to first establish a connection to azure by loging into your azure account using `az login`. You can then follow the example as below. Firstly we get the token from login and use this to authenticate the different python function calls.

  ```python
  from azure.cli.core import get_default_cli
  from azure.cli.core._profile import Profile
  from azure_functions_devops_build.organization.organization_manager import OrganizationManager
  import pprint


  cli_ctx = get_default_cli()
  profile = Profile(cli_ctx=cli_ctx)
  creds, _, _ = profile.get_login_credentials(subscription_id=None)


  organization_manager = OrganizationManager(creds=creds)


  organizations = organization_manager.list_organizations()


  for organization in organizations.value:
      pprint.pprint(organization.__dict__)
  ```



  This Python library extensively uses the Azure DevOps REST APIs and Azure Devops Python API. See the [Azure DevOps REST API reference](https://docs.microsoft.com/en-us/rest/api/vsts/?view=vsts-rest-5.0) for details on calling different APIs and [Azure DevOps Python SDK](https://github.com/Microsoft/azure-devops-python-api) for details on the azure-devops-python-api.



  See samples by looking at tests or viewing the [az-cli functionapp devops-build module](https://github.com/Azure/azure-cli/tree/dev/src/command_modules/azure-cli-appservice/azure/cli/command_modules/appservice).



  Several things need to be setup before you can run the tests:
  1. Signed into the az cli. You can do this by using `az login`.
  2. Since this directly deploys to azure functions, [create an azure functions functionapp using the azure portal](https://docs.microsoft.com/en-us/azure/azure-functions/functions-create-first-azure-function). You need to make a functionapp for these tests to successfully run and make sure you record the details of the subscription name, project name, application type and storage name.
  3. Follow the tests/_config_example.py file, create a tests/_config.py with your own testing environment.
  4. Run the full test suite using `python -m tests.suite`
  5. To run specific manager tests run `python -m tests.{NAME_OF_MANAGER}` eg. `python -m tests.test_builder_manager`



  This project welcomes contributions and suggestions.  Most contributions require you to agree to a
  Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
  the rights to use your contribution. For details, visit https://cla.microsoft.com.

  When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
  a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
  provided by the bot. You will only need to do this once across all repos using our CLA.

  This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
  For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
  contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

location_href:
  00010476-python-azure-functions-devops-build/python3-azure-functions-devops-build-0.0.22-1.noarch.rpm


pkgId: 86e88f481294898928ea76d463a76c1cf27f6de282285ec10f65e17964832175
name: python3-azure-monitor-opentelemetry-exporter
arch: noarch
version: 1.0.0b12
epoch: 0
release: 1
summary: Microsoft Azure Monitor Opentelemetry Exporter Client Library for Python
rpm_license: MIT License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-5d9b69ef-f9d1-4278-9611-e078499518a5
rpm_sourcerpm: python-azure-monitor-opentelemetry-exporter-1.0.0b12-1.src.rpm
checksum_type: sha256
description:
  The exporter for Azure Monitor allows you to export data utilizing the OpenTelemetry SDK and send telemetry data to Azure Monitor for applications written in Python.

  [Source code](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/monitor/azure-monitor-opentelemetry-exporter) | [Package (PyPi)][pypi] | [API reference documentation][api_docs] | [Product documentation][product_docs] | [Samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/monitor/azure-monitor-opentelemetry-exporter/samples) | [Changelog](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/monitor/azure-monitor-opentelemetry-exporter/CHANGELOG.md)





  Install the Microsoft OpenTelemetry exporter for Azure Monitor with [pip][pip]:

  ```Bash
  pip install azure-monitor-opentelemetry-exporter --pre
  ```



  To use this package, you must have:

  * Azure subscription - [Create a free account][azure_sub]
  * Azure Monitor - [How to use application insights][application_insights_namespace]
  * OpenTelemetry SDK - [OpenTelemetry SDK for Python][ot_sdk_python]
  * Python 3.7 or later - [Install Python][python]



  Interaction with Azure monitor exporter starts with an instance of the `AzureMonitorTraceExporter` class for distributed tracing, `AzureMonitorLogExporter` for logging and `AzureMonitorMetricExporter` for metrics. You will need a **connection_string** to instantiate the object.
  Please find the samples linked below for demonstration as to how to construct the exporter using a connection string.



  NOTE: The logging signal for the `AzureMonitorLogExporter` is currently in an EXPERIMENTAL state. Possible breaking changes may ensue in the future.

  ```Python
  from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter
  exporter = AzureMonitorLogExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )
  ```



  ```Python
  from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter
  exporter = AzureMonitorMetricExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )
  ```



  ```Python
  from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
  exporter = AzureMonitorTraceExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )
  ```

  You can also instantiate the exporter directly via the constructor. In this case, the connection string will be automatically populated from the `APPLICATIONINSIGHTS_CONNECTION_STRING` environment variable.

  ```python
  from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter
  exporter = AzureMonitorLogExporter()
  ```

  ```python
  from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter
  exporter = AzureMonitorMetricExporter()
  ```

  ```python
  from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
  exporter = AzureMonitorTraceExporter()
  ```



  Some of the key concepts for the Azure monitor exporter include:

  * [OpenTelemetry][opentelemetry_spec]: OpenTelemetry is a set of libraries used to collect and export telemetry data (metrics, logs, and traces) for analysis in order to understand your software's performance and behavior.

  * [Instrumentation][instrumentation_library]: The ability to call the OpenTelemetry API directly by any application is facilitated by instrumentation. A library that enables OpenTelemetry observability for another library is called an instrumentation Library.

  * [Log][log_concept]: Log refers to capturing of logging, exception and events.

  * [LogRecord][log_record]: Represents a log record emitted from a supported logging library.

  * [Logger][logger]: Converts a `LogRecord` into a readable `LogData`, and will be pushed through the SDK to be exported.

  * [Logger Provider][logger_provider]: Provides a `Logger` for the given instrumentation library.

  * [LogRecordProcessor][log_record_processor]: Interface to hook the log record emitting action.

  * [LoggingHandler][logging_handler]: A handler class which writes logging records in OpenTelemetry format from the standard Python `logging` library.

  * [AzureMonitorLogExporter][log_reference]: This is the class that is initialized to send logging related telemetry to Azure Monitor.

  * [Metric][metric_concept]: `Metric` refers to recording raw measurements with predefined aggregation and sets of attributes for a period in time.

  * [Measurement][measurement]: Represents a data point recorded at a point in time.

  * [Instrument][instrument]: Instruments are used to report `Measurement`s.

  * [Meter][meter]: The `Meter` is responsible for creating `Instruments`.

  * [Meter Provider][meter_provider]: Provides a `Meter` for the given instrumentation library.

  * [Metric Reader][metric_reader]: An SDK implementation object that provides the common configurable aspects of the OpenTelemetry Metrics SDK such as collection, flushing and shutdown.

  * [AzureMonitorMetricExporter][metric_reference]: This is the class that is initialized to send metric related telemetry to Azure Monitor.

  * [Trace][trace_concept]: Trace refers to distributed tracing. A distributed trace is a set of events, triggered as a result of a single logical operation, consolidated across various components of an application. In particular, a Trace can be thought of as a directed acyclic graph (DAG) of Spans, where the edges between Spans are defined as parent/child relationship.

  * [Span][span]: Represents a single operation within a `Trace`. Can be nested to form a trace tree. Each trace contains a root span, which typically describes the entire operation and, optionally, one ore more sub-spans for its sub-operations.

  * [Tracer][tracer]: Responsible for creating `Span`s.

  * [Tracer Provider][tracer_provider]: Provides a `Tracer` for use by the given instrumentation library.

  * [Span Processor][span_processor]: A span processor allows hooks for SDK's `Span` start and end method invocations. Follow the link for more information.

  * [AzureMonitorTraceExporter][trace_reference]: This is the class that is initialized to send tracing related telemetry to Azure Monitor.

  * [Sampling][sampler_ref]: Sampling is a mechanism to control the noise and overhead introduced by OpenTelemetry by reducing the number of samples of traces collected and sent to the backend.

  * ApplicationInsightsSampler: Application Insights specific sampler used for consistent sampling across Application Insights SDKs and OpenTelemetry-based SDKs sending data to Application Insights. This sampler MUST be used whenever `AzureMonitorTraceExporter` is used.

  For more information about these resources, see [What is Azure Monitor?][product_docs].



  All configuration options can be passed through the constructors of exporters through `kwargs`. Below is a list of configurable options.

  * `connection_string`: The connection string used for your Application Insights resource.
  * `disable_offline_storage`: Boolean value to determine whether to disable storing failed telemetry records for retry. Defaults to `False`.
  * `storage_directory`: Storage directory in which to store retry files. Defaults to `<tempfile.gettempdir()>/Microsoft/AzureMonitor/opentelemetry-python-<your-instrumentation-key>`.





  NOTE: The logging signal for the `AzureMonitorLogExporter` is currently in an EXPERIMENTAL state. Possible breaking changes may ensue in the future.

  The following sections provide several code snippets covering some of the most common tasks, including:

  * [Exporting a log record](#export-hello-world-log)
  * [Exporting correlated log record](#export-correlated-log)
  * [Exporting log record with custom properties](#export-custom-properties-log)
  * [Exporting an exceptions log record](#export-exceptions-log)

  Review the [OpenTelemetry Logging SDK][ot_logging_sdk] to learn how to use OpenTelemetry components to collect logs.



  ```Python
  """
  An example to show an application using Opentelemetry logging sdk. Logging calls to the standard Python
  logging library are tracked and telemetry is exported to application insights with the AzureMonitorLogExporter.
  """
  import os
  import logging

  from opentelemetry.sdk._logs import (
      LoggerProvider,
      LoggingHandler,
      set_logger_provider,
  )
  from opentelemetry.sdk._logs.export import BatchLogRecordProcessor

  from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter

  logger_provider = LoggerProvider()
  set_logger_provider(logger_provider)

  exporter = AzureMonitorLogExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )

  logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))
  handler = LoggingHandler()


  logging.getLogger().addHandler(handler)
  logging.getLogger().setLevel(logging.NOTSET)

  logger = logging.getLogger(__name__)

  logger.warning("Hello World!")
  ```



  ```Python
  """
  An example showing how to include context correlation information in logging telemetry.
  """
  import os
  import logging

  from opentelemetry import trace
  from opentelemetry.sdk._logs import (
      LoggerProvider,
      LoggingHandler,
      set_logger_provider,
  )
  from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
  from opentelemetry.sdk.trace import TracerProvider

  from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter

  trace.set_tracer_provider(TracerProvider())
  tracer = trace.get_tracer(__name__)
  logger_provider = LoggerProvider()
  set_logger_provider(logger_provider)

  exporter = AzureMonitorLogExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )

  logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))
  handler = LoggingHandler()


  logging.getLogger().addHandler(handler)
  logging.getLogger().setLevel(logging.NOTSET)

  logger = logging.getLogger(__name__)

  logger.info("INFO: Outside of span")
  with tracer.start_as_current_span("foo"):
      logger.warning("WARNING: Inside of span")
  logger.error("ERROR: After span")
  ```



  ```Python
  """
  An example showing how to add custom properties to logging telemetry.
  """
  import os
  import logging

  from opentelemetry.sdk._logs import (
      LoggerProvider,
      LoggingHandler,
      set_logger_provider,
  )
  from opentelemetry.sdk._logs.export import BatchLogRecordProcessor

  from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter

  logger_provider = LoggerProvider()
  set_logger_provider(logger_provider)

  exporter = AzureMonitorLogExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )

  logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))
  handler = LoggingHandler()


  logging.getLogger().addHandler(handler)
  logging.getLogger().setLevel(logging.NOTSET)

  logger = logging.getLogger(__name__)


  logger.debug("DEBUG: Debug with properties", extra={"debug": "true"})
  ```



  ```Python
  """
  An example showing how to export exception telemetry using the AzureMonitorLogExporter.
  """
  import os
  import logging

  from opentelemetry._logs import (
      get_logger_provider,
      set_logger_provider,
  )
  from opentelemetry.sdk._logs import (
      LoggerProvider,
      LoggingHandler,
  )
  from opentelemetry.sdk._logs.export import BatchLogRecordProcessor

  from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter

  set_logger_provider(LoggerProvider())
  exporter = AzureMonitorLogExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )
  get_logger_provider().add_log_record_processor(BatchLogRecordProcessor(exporter))


  handler = LoggingHandler()
  logger = logging.getLogger(__name__)
  logger.addHandler(handler)
  logger.setLevel(logging.NOTSET)



  try:
      val = 1 / 0
      print(val)
  except ZeroDivisionError:
      logger.exception("Error: Division by zero")

  try:
      val = 1 / 0
      print(val)
  except ZeroDivisionError:
      logger.error("Error: Division by zero", stack_info=True, exc_info=True)
  ```



  The following sections provide several code snippets covering some of the most common tasks, including:

  * [Using different metric instruments](#metric-instrument-usage)
  * [Customizing outputted metrics with views](#metric-custom-views)
  * [Recording instruments with attributes](#metric-record-attributes)

  Review the [OpenTelemetry Metrics SDK][ot_metrics_sdk] to learn how to use OpenTelemetry components to collect metrics.



  ```python
  """
  An example to show an application using all instruments in the OpenTelemetry SDK. Metrics created
  and recorded using the sdk are tracked and telemetry is exported to application insights with the
  AzureMonitorMetricsExporter.
  """
  import os
  from typing import Iterable

  from opentelemetry import metrics
  from opentelemetry.metrics import CallbackOptions, Observation
  from opentelemetry.sdk.metrics import MeterProvider
  from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader

  from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter

  exporter = AzureMonitorMetricExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )
  reader = PeriodicExportingMetricReader(exporter, export_interval_millis=5000)
  metrics.set_meter_provider(MeterProvider(metric_readers=[reader]))


  meter = metrics.get_meter_provider().get_meter("sample")


  def observable_counter_func(options: CallbackOptions) -> Iterable[Observation]:
      yield Observation(1, {})


  def observable_up_down_counter_func(
      options: CallbackOptions,
  ) -> Iterable[Observation]:
      yield Observation(-10, {})


  def observable_gauge_func(options: CallbackOptions) -> Iterable[Observation]:
      yield Observation(9, {})


  counter = meter.create_counter("counter")
  counter.add(1)


  observable_counter = meter.create_observable_counter(
      "observable_counter", [observable_counter_func]
  )


  up_down_counter = meter.create_up_down_counter("up_down_counter")
  up_down_counter.add(1)
  up_down_counter.add(-5)


  observable_up_down_counter = meter.create_observable_up_down_counter(
      "observable_up_down_counter", [observable_up_down_counter_func]
  )


  histogram = meter.create_histogram("histogram")
  histogram.record(99.9)


  gauge = meter.create_observable_gauge("gauge", [observable_gauge_func])

  ```



  ```python
  """
  This example shows how to customize the metrics that are output by the SDK using Views. Metrics created
  and recorded using the sdk are tracked and telemetry is exported to application insights with the
  AzureMonitorMetricsExporter.
  """
  import os

  from opentelemetry import metrics
  from opentelemetry.sdk.metrics import Counter, MeterProvider
  from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
  from opentelemetry.sdk.metrics.view import View

  from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter

  exporter = AzureMonitorMetricExporter.from_connection_string(
      os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )


  change_metric_name_view = View(
      instrument_type=Counter,
      instrument_name="my.counter",
      name="my.counter.total",
  )

  reader = PeriodicExportingMetricReader(exporter, export_interval_millis=5000)
  provider = MeterProvider(
      metric_readers=[
          reader,
      ],
      views=[
          change_metric_name_view,
      ],
  )
  metrics.set_meter_provider(provider)

  meter = metrics.get_meter_provider().get_meter("view-name-change")
  my_counter = meter.create_counter("my.counter")
  my_counter.add(100)

  ```

  More examples with the metrics `Views` SDK can be found [here](https://github.com/open-telemetry/opentelemetry-python/tree/main/docs/examples/metrics/views).



  ```python
  """
  An example to show an application using different attributes with instruments in the OpenTelemetry SDK.
  Metrics created and recorded using the sdk are tracked and telemetry is exported to application insights
  with the AzureMonitorMetricsExporter.
  """
  import os

  from opentelemetry import metrics
  from opentelemetry.sdk.metrics import MeterProvider
  from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader

  from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter

  exporter = AzureMonitorMetricExporter.from_connection_string(
      os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )
  reader = PeriodicExportingMetricReader(exporter, export_interval_millis=5000)
  metrics.set_meter_provider(MeterProvider(metric_readers=[reader]))

  attribute_set1 = {
      "key1": "val1"
  }
  attribute_set2 = {
      "key2": "val2"
  }
  large_attribute_set = {}
  for i in range(20):
      key = "key{}".format(i)
      val = "val{}".format(i)
      large_attribute_set[key] = val

  meter = metrics.get_meter_provider().get_meter("sample")


  counter = meter.create_counter("attr1_counter")
  counter.add(1, attribute_set1)


  counter2 = meter.create_counter("attr2_counter")
  counter2.add(10, attribute_set1)
  counter2.add(30, attribute_set2)


  counter3 = meter.create_counter("large_attr_counter")
  counter3.add(100, attribute_set1)
  counter3.add(200, large_attribute_set)

  ```



  The following sections provide several code snippets covering some of the most common tasks, including:

  * [Exporting a custom span](#export-hello-world-trace)
  * [Using an instrumentation to track a library](#instrumentation-with-requests-library)
  * [Enabling sampling to limit the amount of telemetry sent](#enabling-sampling)

  Review the [OpenTelemetry Tracing SDK][ot_tracing_sdk] to learn how to use OpenTelemetry components to collect logs.



  ```Python
  """
  An example to show an application using Opentelemetry tracing api and sdk. Custom dependencies are
  tracked via spans and telemetry is exported to application insights with the AzureMonitorTraceExporter.
  """
  import os
  from opentelemetry import trace
  from opentelemetry.sdk.trace import TracerProvider
  from opentelemetry.sdk.trace.export import BatchSpanProcessor
  from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

  trace.set_tracer_provider(TracerProvider())
  tracer = trace.get_tracer(__name__)

  exporter = AzureMonitorTraceExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )
  span_processor = BatchSpanProcessor(exporter)
  trace.get_tracer_provider().add_span_processor(span_processor)

  with tracer.start_as_current_span("hello"):
      print("Hello, World!")
  ```



  OpenTelemetry also supports several instrumentations which allows to instrument with third party libraries.

  For a list of instrumentations available in OpenTelemetry, visit the contrib [documentation](https://opentelemetry-python-contrib.readthedocs.io/en/latest/).

  This example shows how to instrument with the [requests](https://pypi.org/project/requests/) library.

  * Install the requests instrumentation package using pip install opentelemetry-instrumentation-requests.

  ```Python
  """
  An example to show an application instrumented with the OpenTelemetry requests instrumentation.
  Calls made with the requests library will be automatically tracked and telemetry is exported to
  application insights with the AzureMonitorTraceExporter.
  See more info on the requests instrumentation here:
  https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation/opentelemetry-instrumentation-requests
  """
  import os
  import requests
  from opentelemetry import trace
  from opentelemetry.instrumentation.requests import RequestsInstrumentor
  from opentelemetry.sdk.trace import TracerProvider
  from opentelemetry.sdk.trace.export import BatchSpanProcessor
  from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter


  RequestsInstrumentor().instrument()

  trace.set_tracer_provider(TracerProvider())
  tracer = trace.get_tracer(__name__)
  exporter = AzureMonitorTraceExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )
  span_processor = BatchSpanProcessor(exporter)
  trace.get_tracer_provider().add_span_processor(span_processor)


  response = requests.get(url="https://azure.microsoft.com/")
  ```



  You can enable sampling to limit the amount of telemetry records you receive. In order to enable correct sampling in Application Insights, use the `ApplicationInsightsSampler` as shown below.

  ```Python
  """
  An example to show an application using the ApplicationInsightsSampler to enable sampling for your telemetry.
  Specify a sampling rate for the sampler to limit the amount of telemetry records you receive. Custom dependencies
   are tracked via spans and telemetry is exported to application insights with the AzureMonitorTraceExporter.
  """
  import os
  from opentelemetry import trace
  from opentelemetry.sdk.trace import TracerProvider
  from opentelemetry.sdk.trace.export import BatchSpanProcessor
  from azure.monitor.opentelemetry.exporter import (
      ApplicationInsightsSampler,
      AzureMonitorTraceExporter,
  )



  sampler = ApplicationInsightsSampler(0.75)
  trace.set_tracer_provider(TracerProvider(sampler=sampler))
  tracer = trace.get_tracer(__name__)
  exporter = AzureMonitorTraceExporter(
      connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]
  )
  span_processor = BatchSpanProcessor(exporter)
  trace.get_tracer_provider().add_span_processor(span_processor)

  for i in range(100):
      
      with tracer.start_as_current_span("hello"):
          print("Hello, World!")
  ```



  The exporter raises exceptions defined in [Azure Core](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/README.md#azure-core-library-exceptions).





  Please find further examples in the [samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/monitor/azure-monitor-opentelemetry-exporter/samples) directory demonstrating common scenarios.



  For more extensive documentation on the Azure Monitor service, see the [Azure Monitor documentation][product_docs] on docs.microsoft.com.

  For detailed overview of OpenTelemetry, visit their [overview](https://github.com/open-telemetry/opentelemetry-specification/blob/master/specification/overview.md) page.

  For the official OpenTelemetry Python documentation and how to enable other telemetry scenarios, visit the official OpenTelemetry [website](https://opentelemetry.io/docs/instrumentation/python/).

  For more information on the Azure Monitor OpenTelemetry Distro, which is a bundle of useful, pre-assembled components (one of them being this current package) that enable telemetry scenarios with Azure Monitor, visit the [README](https://github.com/microsoft/ApplicationInsights-Python/tree/main/azure-monitor-opentelemetry-distro).



  This project welcomes contributions and suggestions.  Most contributions require you to agree to a
  Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
  the rights to use your contribution. For details, visit https://cla.microsoft.com.

  When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
  a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
  provided by the bot. You will only need to do this once across all repos using our CLA.

  This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
  For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
  contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

  <!-- LINKS -->
  [api_docs]: https://azuresdkdocs.blob.core.windows.net/$web/python/azure-opentelemetry-exporter-azuremonitor/1.0.0b2/index.html
  [product_docs]: https://docs.microsoft.com/azure/azure-monitor/overview
  [azure_sub]: https://azure.microsoft.com/free/
  [pip]: https://pypi.org/project/pip/
  [pypi]: https://pypi.org/project/azure-monitor-opentelemetry-exporter/
  [python]: https://www.python.org/downloads/
  [ot_sdk_python]: https://github.com/open-telemetry/opentelemetry-python
  [application_insights_namespace]: https://docs.microsoft.com/azure/azure-monitor/app/app-insights-overview#how-do-i-use-application-insights
  [opentelemetry_spec]: https://opentelemetry.io/
  [instrumentation_library]: https://github.com/open-telemetry/opentelemetry-specification/blob/master/specification/overview.md#instrumentation-libraries
  [log_concept]: https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/overview.md#log-signal
  [log_record]: https://opentelemetry-python.readthedocs.io/en/stable/sdk/logs.html#opentelemetry.sdk._logs.LogRecord
  [logger]: https://opentelemetry-python.readthedocs.io/en/stable/sdk/logs.html#opentelemetry.sdk._logs.Logger
  [logger_provider]: https://opentelemetry-python.readthedocs.io/en/stable/sdk/logs.html#opentelemetry.sdk._logs.LoggerProvider
  [log_record_processor]: https://opentelemetry-python.readthedocs.io/en/stable/sdk/logs.html#opentelemetry.sdk._logs.LogRecordProcessor
  [logging_handler]: https://opentelemetry-python.readthedocs.io/en/stable/sdk/logs.html#opentelemetry.sdk._logs.LoggingHandler
  [log_reference]: https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/monitor/azure-monitor-opentelemetry-exporter/azure/monitor/opentelemetry/exporter/export/logs/_exporter.py
  [ot_logging_sdk]: https://opentelemetry-python.readthedocs.io/en/stable/sdk/logs.html
  [metric_concept]: https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/overview.md#metric-signal
  [measurement]: https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/api.md#measurement
  [instrument]: https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/api.md#instrument
  [meter]: https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/api.md#meter
  [meter_provider]: https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/api.md#meterprovider
  [metric_reader]:https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#metricreader
  [metric_reference]: https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/monitor/azure-monitor-opentelemetry-exporter/azure/monitor/opentelemetry/exporter/export/metrics/_exporter.py
  [ot_metrics_sdk]: https://opentelemetry-python.readthedocs.io/en/stable/sdk/metrics.html
  [trace_concept]: https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/overview.md#tracing-signal
  [span]: https://opentelemetry-python.readthedocs.io/en/stable/api/trace.html?highlight=TracerProvider#opentelemetry.trace.Span
  [tracer]: https://opentelemetry-python.readthedocs.io/en/stable/api/trace.html?highlight=TracerProvider#opentelemetry.trace.Tracer
  [tracer_provider]: https://opentelemetry-python.readthedocs.io/en/stable/api/trace.html?highlight=TracerProvider#opentelemetry.trace.TracerProvider
  [span_processor]: https://opentelemetry-python.readthedocs.io/en/stable/_modules/opentelemetry/sdk/trace.html?highlight=SpanProcessor#
  [trace_reference]: https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/monitor/azure-monitor-opentelemetry-exporter/azure/monitor/opentelemetry/exporter/export/trace/_exporter.py
  [ot_tracing_sdk]: https://opentelemetry-python.readthedocs.io/en/stable/sdk/trace.html
  [sampler_ref]: https://github.com/open-telemetry/opentelemetry-specification/blob/master/specification/trace/sdk.md#sampling

url:
  https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/monitor/azure-monitor-opentelemetry-exporter

location_href:
  00013964-python-azure-monitor-opentelemetry-exporter/python3-azure-monitor-opentelemetry-exporter-1.0.0b12-1.noarch.rpm


pkgId: f7c9806e59892426e7d121c4c5da55d693ea3d3134af049846aa5f57b07fd3b6
name: python3-azure-servicemanagement-legacy
arch: noarch
version: 0.20.7
epoch: 0
release: 1
summary: Microsoft Azure Legacy Service Management Client Library for Python
url: https://github.com/Azure/azure-sdk-for-python
rpm_license: Apache License 2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-d0d70b76-fc89-4d75-a944-c134aad7e211
rpm_sourcerpm: python-azure-servicemanagement-legacy-0.20.7-1.src.rpm
checksum_type: sha256
description:
  This is the Microsoft Azure Service Management Legacy Client Library.

  All packages in this bundle have been tested with Python 2.7, 3.3, 3.4 and 3.5.

  For the newer Azure Resource Management (ARM) libraries, see [azure-mgmt](https://pypi.python.org/pypi/azure-mgmt).

  For a more complete set of Azure libraries, see the [azure](https://pypi.python.org/pypi/azure) bundle package.




  **IMPORTANT**: If you have an earlier version of the azure package
  (version < 1.0), you should uninstall it before installing this package.

  You can check the version using pip:

  ```shell
  pip freeze
  ```

  If you see azure==0.11.0 (or any version below 1.0), uninstall it first:

  ```shell
  pip uninstall azure
  ```



  -  Cloud Service management (Virtual Machines, VM Images, OS Images)
  -  Storage accounts management
  -  Scheduler management
  -  Service Bus management
  -  Affinity Group management
  -  Management certificate management
  -  Web Apps (Website) management






  To install via the Python Package Index (PyPI), type:

  ```shell
  pip install azure-servicemanagement-legacy
  ```




  To get the source code of the SDK via **git** type:

  ```shell
  git clone https://github.com/Azure/azure-sdk-for-python.git
  cd azure-sdk-for-python
  cd azure-servicemanagement-legacy
  python setup.py install
  ```








  You will need two certificates, one for the server (a .cer file) and one for
  the client (a .pem file).



  You can download your Azure publish settings file and use the certificate that
  is embedded in that file to create the client certificate. The server
  certificate already exists, so you won't need to upload one.

  To do this, download your [publish settings](http://go.microsoft.com/fwlink/?LinkID=301775)
  then use this code to create the .pem file.

  ```python
  from azure.servicemanagement import get_certificate_from_publish_settings

  subscription_id = get_certificate_from_publish_settings(
      publish_settings_path='MyAccount.PublishSettings',
      path_to_write_certificate='mycert.pem',
      subscription_id='00000000-0000-0000-0000-000000000000',
  )
  ```

  The subscription id parameter is optional. If there are more than one
  subscription in the publish settings, the first one will be used.



  To create the .pem file using [OpenSSL](http://www.openssl.org), execute this:

  ```shell
  openssl req -x509 -nodes -days 365 -newkey rsa:1024 -keyout mycert.pem -out mycert.pem
  ```
  To create the .cer certificate, execute this:

  ```shell
  openssl x509 -inform pem -in mycert.pem -outform der -out mycert.cer
  ```

  After you have created the certificate, you will need to upload the .cer
  file to Microsoft Azure via the "Upload" action of the "Settings" tab of
  the [management portal](http://manage.windowsazure.com).






  To initialize the management service, pass in your subscription id and
  the path to the .pem file.

  ```python
  from azure.servicemanagement import ServiceManagementService
  subscription_id = '00000000-0000-0000-0000-000000000000'
  cert_file = 'mycert.pem'
  sms = ServiceManagementService(subscription_id, cert_file)
  ```



  ```python
  locations = sms.list_locations()
  for location in locations:
      print(location.name)
  ```



  To create a storage service, you need a name for the service (between 3
  and 24 lowercase characters and unique within Microsoft Azure), a label
  (up to 100 characters, automatically encoded to base-64), and either a
  location or an affinity group.

  ```python
  name = "mystorageservice"
  desc = name
  label = name
  location = 'West US'

  result = sms.create_storage_account(name, desc, label, location=location)
  sms.wait_for_operation_status(result.request_id, timeout=30)
  ```



  A cloud service is also known as a hosted service (from earlier versions
  of Microsoft Azure). The **create\_hosted\_service** method allows you
  to create a new hosted service by providing a hosted service name (which
  must be unique in Microsoft Azure), a label (automatically encoded to
  base-64), and the location *or* the affinity group for your service.

  ```python
  name = "myhostedservice"
  desc = name
  label = name
  location = 'West US'

  result = sms.create_hosted_service(name, label, desc, location=location)
  sms.wait_for_operation_status(result.request_id, timeout=30)
  ```



  To create a virtual machine, you first need to create a cloud service.
  Then create the virtual machine deployment using the
  create_virtual_machine_deployment method.

  ```python
  from azure.servicemanagement import LinuxConfigurationSet, OSVirtualHardDisk

  name = "myhostedservice"


  image_name = 'OpenLogic__OpenLogic-CentOS-62-20120531-en-us-30GB.vhd'



  media_link = 'url_to_target_storage_blob_for_vm_hd'



  linux_config = LinuxConfigurationSet(
      'myhostname',
      'myuser',
      'mypassword',
      disable_ssh_password_authentication=True,
  )

  os_hd = OSVirtualHardDisk(image_name, media_link)

  result = sms.create_virtual_machine_deployment(
      service_name=name,
      deployment_name=name,
      deployment_slot='production',
      label=name,
      role_name=name,
      system_config=linux_config,
      os_virtual_hard_disk=os_hd,
      role_size='Small',
  )
  sms.wait_for_operation_status(result.request_id, timeout=600)
  ```




  Be sure to check out the Microsoft Azure [Developer Forums on Stack
  Overflow](http://go.microsoft.com/fwlink/?LinkId=234489) if you have
  trouble with the provided code.




  If you would like to become an active contributor to this project please
  follow the instructions provided in [Microsoft Azure Projects
  Contribution
  Guidelines](http://azure.github.io/guidelines.html).

  If you encounter any bugs with the library please file an issue in the
  [Issues](https://github.com/Azure/azure-sdk-for-python/issues)
  section of the project.




  [Microsoft Azure Python Developer
  Center](http://azure.microsoft.com/en-us/develop/python/)


  ![Impressions](https://azure-sdk-impressions.azurewebsites.net/api/impressions/azure-sdk-for-python%2Fazure-servicemanagement-legacy%2FREADME.png)

location_href:
  00009433-python-azure-servicemanagement-legacy/python3-azure-servicemanagement-legacy-0.20.7-1.noarch.rpm


pkgId: b5b5d271d9b6a33b46af16723ea80f3ed53ca30a306219b86c4940b71e54db26
name: python3-azure-storage-file-datalake
arch: noarch
version: 12.10.1
epoch: 0
release: 1
summary: Microsoft Azure File DataLake Storage Client Library for Python
url: https://github.com/Azure/azure-sdk-for-python
rpm_license: MIT License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-c24f5fda-3e26-4f86-9e9c-4a75e94be14e
rpm_sourcerpm: python-azure-storage-file-datalake-12.10.1-1.src.rpm
checksum_type: sha256
description:
  Overview

  This preview package for Python includes ADLS Gen2 specific API support made available in Storage SDK. This includes:
  1. New directory level operations (Create, Rename, Delete) for hierarchical namespace enabled (HNS) storage account. For HNS enabled accounts, the rename/move operations are atomic.
  2. Permission related operations (Get/Set ACLs) for hierarchical namespace enabled (HNS) accounts.


  [Source code](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-file-datalake/azure/storage/filedatalake) | [Package (PyPi)](https://pypi.org/project/azure-storage-file-datalake/) | [API reference documentation](https://aka.ms/azsdk-python-storage-filedatalake-ref) | [Product documentation](https://docs.microsoft.com/azure/storage/) | [Samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-file-datalake/samples)





  * Python 3.7 or later is required to use this package. For more details, please read our page on [Azure SDK for Python version support policy](https://github.com/Azure/azure-sdk-for-python/wiki/Azure-SDKs-Python-version-support-policy).
  * You must have an [Azure subscription](https://azure.microsoft.com/free/) and an
  [Azure storage account](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-quickstart-create-account) to use this package.


  Install the Azure DataLake Storage client library for Python with [pip](https://pypi.org/project/pip/):

  ```bash
  pip install azure-storage-file-datalake --pre
  ```


  If you wish to create a new storage account, you can use the
  [Azure Portal](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-quickstart-create-account#create-an-account-using-the-azure-portal),
  [Azure PowerShell](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-quickstart-create-account#create-an-account-using-powershell),
  or [Azure CLI](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-quickstart-create-account#create-an-account-using-azure-cli):

  ```bash


  az group create --name my-resource-group --location westus2


  az extension add --name storage-preview


  az storage account create --name my-storage-account-name --resource-group my-resource-group --sku Standard_LRS --kind StorageV2 --hierarchical-namespace true
  ```



  Interaction with DataLake Storage starts with an instance of the DataLakeServiceClient class. You need an existing storage account, its URL, and a credential to instantiate the client object.



  To authenticate the client you have a few options:
  1. Use a SAS token string
  2. Use an account shared access key
  3. Use a token credential from [azure.identity](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity)

  Alternatively, you can authenticate with a storage connection string using the `from_connection_string` method. See example: [Client creation with a connection string](#client-creation-with-a-connection-string).

  You can omit the credential if your account URL already has a SAS token.



  Once you have your account URL and credentials ready, you can create the DataLakeServiceClient:

  ```python
  from azure.storage.filedatalake import DataLakeServiceClient

  service = DataLakeServiceClient(account_url="https://<my-storage-account-name>.dfs.core.windows.net/", credential=credential)
  ```



  DataLake storage offers four types of resources:
  * The storage account
  * A file system in the storage account
  * A directory under the file system
  * A file in a the file system or under directory


  This library includes a complete async API supported on Python 3.5+. To use it, you must
  first install an async transport, such as [aiohttp](https://pypi.org/project/aiohttp/).
  See
  [azure-core documentation](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/CLIENT_LIBRARY_DEVELOPER.md#transport)
  for more information.

  Async clients and credentials should be closed when they're no longer needed. These
  objects are async context managers and define async `close` methods.



  The DataLake Storage SDK provides four different clients to interact with the DataLake Service:
  1. **DataLakeServiceClient** - this client interacts with the DataLake Service at the account level.
      It provides operations to retrieve and configure the account properties
      as well as list, create, and delete file systems within the account.
      For operations relating to a specific file system, directory or file, clients for those entities
      can also be retrieved using the `get_file_client`, `get_directory_client` or `get_file_system_client` functions.
  2. **FileSystemClient** - this client represents interaction with a specific
      file system, even if that file system does not exist yet. It provides operations to create, delete, or
      configure file systems and includes operations to list paths under file system, upload, and delete file or
      directory in the file system.
      For operations relating to a specific file, the client can also be retrieved using
      the `get_file_client` function.
      For operations relating to a specific directory, the client can be retrieved using
      the `get_directory_client` function.
  3. **DataLakeDirectoryClient** - this client represents interaction with a specific
      directory, even if that directory does not exist yet. It provides directory operations create, delete, rename,
      get properties and set properties operations.
  3. **DataLakeFileClient** - this client represents interaction with a specific
      file, even if that file does not exist yet. It provides file operations to append data, flush data, delete,
      create, and read file.
  4. **DataLakeLeaseClient** - this client represents lease interactions with a FileSystemClient, DataLakeDirectoryClient
      or DataLakeFileClient. It provides operations to acquire, renew, release, change, and break leases on the resources.



  The following sections provide several code snippets covering some of the most common Storage DataLake tasks, including:

  * [Client creation with a connection string](#client-creation-with-a-connection-string)
  * [Uploading a file](#uploading-a-file)
  * [Downloading a file](#downloading-a-file)
  * [Enumerating paths](#enumerating-paths)



  Create the DataLakeServiceClient using the connection string to your Azure Storage account.

  ```python
  from azure.storage.filedatalake import DataLakeServiceClient

  service = DataLakeServiceClient.from_connection_string(conn_str="my_connection_string")
  ```


  Upload a file to your file system.

  ```python
  from azure.storage.filedatalake import DataLakeFileClient

  data = b"abc"
  file = DataLakeFileClient.from_connection_string("my_connection_string",
                                                   file_system_name="myfilesystem", file_path="myfile")
  file.create_file ()
  file.append_data(data, offset=0, length=len(data))
  file.flush_data(len(data))
  ```


  Download a file from your file system.

  ```python
  from azure.storage.filedatalake import DataLakeFileClient

  file = DataLakeFileClient.from_connection_string("my_connection_string",
                                                   file_system_name="myfilesystem", file_path="myfile")

  with open("./BlockDestination.txt", "wb") as my_file:
      download = file.download_file()
      download.readinto(my_file)
  ```


  List the paths in your file system.

  ```python
  from azure.storage.filedatalake import FileSystemClient

  file_system = FileSystemClient.from_connection_string("my_connection_string", file_system_name="myfilesystem")

  paths = file_system.get_paths()
  for path in paths:
      print(path.name + '\n')
  ```



  Optional keyword arguments that can be passed in at the client and per-operation level.



  Use the following keyword arguments when instantiating a client to configure the retry policy:

  * __retry_total__ (int): Total number of retries to allow. Takes precedence over other counts.
  Pass in `retry_total=0` if you do not want to retry on requests. Defaults to 10.
  * __retry_connect__ (int): How many connection-related errors to retry on. Defaults to 3.
  * __retry_read__ (int): How many times to retry on read errors. Defaults to 3.
  * __retry_status__ (int): How many times to retry on bad status codes. Defaults to 3.
  * __retry_to_secondary__ (bool): Whether the request should be retried to secondary, if able.
  This should only be enabled of RA-GRS accounts are used and potentially stale data can be handled.
  Defaults to `False`.



  Other optional configuration keyword arguments that can be specified on the client or per-operation.

  **Client keyword arguments:**

  * __connection_timeout__ (int): The number of seconds the client will wait to establish a connection to the server.
  Defaults to 20 seconds.
  * __read_timeout__ (int): The number of seconds the client will wait, between consecutive read operations, for a
  response from the server. This is a socket level timeout and is not affected by overall data size. Client-side read
  timeouts will be automatically retried. Defaults to 60 seconds.
  * __transport__ (Any): User-provided transport to send the HTTP request.

  **Per-operation keyword arguments:**

  * __raw_response_hook__ (callable): The given callback uses the response returned from the service.
  * __raw_request_hook__ (callable): The given callback uses the request before being sent to service.
  * __client_request_id__ (str): Optional user specified identification of the request.
  * __user_agent__ (str): Appends the custom value to the user-agent header to be sent with the request.
  * __logging_enable__ (bool): Enables logging at the DEBUG level. Defaults to False. Can also be passed in at
  the client level to enable it for all requests.
  * __logging_body__ (bool): Enables logging the request and response body. Defaults to False. Can also be passed in at
  the client level to enable it for all requests.
  * __headers__ (dict): Pass in custom headers as key, value pairs. E.g. `headers={'CustomValue': value}`



  DataLake Storage clients raise exceptions defined in [Azure Core](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/README.md).

  This list can be used for reference to catch thrown exceptions. To get the specific error code of the exception, use the `error_code` attribute, i.e, `exception.error_code`.


  This library uses the standard
  [logging](https://docs.python.org/3/library/logging.html) library for logging.
  Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO
  level.

  Detailed DEBUG level logging, including request/response bodies and unredacted
  headers, can be enabled on a client with the `logging_enable` argument:
  ```python
  import sys
  import logging
  from azure.storage.filedatalake import DataLakeServiceClient


  logger = logging.getLogger('azure.storage')
  logger.setLevel(logging.DEBUG)


  handler = logging.StreamHandler(stream=sys.stdout)
  logger.addHandler(handler)


  service_client = DataLakeServiceClient.from_connection_string("your_connection_string", logging_enable=True)
  ```

  Similarly, `logging_enable` can enable detailed logging for a single operation,
  even when it isn't enabled for the client:
  ```py
  service_client.list_file_systems(logging_enable=True)
  ```





  Get started with our [Azure DataLake samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-file-datalake/samples).

  Several DataLake Storage Python SDK samples are available to you in the SDK's GitHub repository. These samples provide example code for additional scenarios commonly encountered while working with DataLake Storage:

  * [`datalake_samples_access_control.py`](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-file-datalake/samples/datalake_samples_access_control.py) - Examples for common DataLake Storage tasks:
      * Set up a file system
      * Create a directory
      * Set/Get access control for the directory
      * Create files under the directory
      * Set/Get access control for each file
      * Delete file system

  * [`datalake_samples_upload_download.py`](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/storage/azure-storage-file-datalake/samples/datalake_samples_upload_download.py) - Examples for common DataLake Storage tasks:
      * Set up a file system
      * Create file
      * Append data to the file
      * Flush data to the file
      * Download the uploaded data
      * Delete file system




  Table for [ADLS Gen1 to ADLS Gen2 API Mapping](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/storage/azure-storage-file-datalake/GEN1_GEN2_MAPPING.md)
  For more extensive REST documentation on Data Lake Storage Gen2, see the [Data Lake Storage Gen2 documentation](https://docs.microsoft.com/rest/api/storageservices/datalakestoragegen2/filesystem) on docs.microsoft.com.



  This project welcomes contributions and suggestions.  Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

  When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

  This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

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summary: A backport of the get_terminal_size function from Python 3.3's shutil.
url: https://github.com/chrippa/backports.shutil_get_terminal_size
rpm_license: MIT
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rpm_group: Unspecified
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  Unlike the original version it is written in pure Python rather than C,
  so it might be a tiny bit slower.

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version: 3.7.0.1
epoch: 0
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summary: The ssl.match_hostname() function from Python 3.5
url: http://bitbucket.org/brandon/backports.ssl_match_hostname
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  if you check the hostname in the certificate returned
  by the server to which you are connecting,
  and verify that it matches to hostname
  that you are trying to reach.
  But the matching logic, defined in `RFC2818`_,
  can be a bit tricky to implement on your own.
  So the ``ssl`` package in the Standard Library of Python 3.2
  and greater now includes a ``match_hostname()`` function
  for performing this check instead of requiring every application
  to implement the check separately.
  This backport brings ``match_hostname()`` to users
  of earlier versions of Python.
  Simply make this distribution a dependency of your package,
  and then use it like this::
      from backports.ssl_match_hostname import match_hostname, CertificateError
      [...]
      sslsock = ssl.wrap_socket(sock, ssl_version=ssl.PROTOCOL_SSLv23,
                                cert_reqs=ssl.CERT_REQUIRED, ca_certs=...)
      try:
          match_hostname(sslsock.getpeercert(), hostname)
      except CertificateError, ce:
  Brandon Craig Rhodes is merely the packager of this distribution;
  the actual code inside comes from Python 3.7 with small changes for
  portability.

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  ```bash
  pip3 install cloudformation-cli-go-plugin
  ```
  Refer to the documentation for the [CloudFormation CLI](https://github.com/aws-cloudformation/cloudformation-cli) for usage instructions.

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version: 2.0.14
epoch: 0
release: 1
summary: please add a summary manually as the author left a blank one
url: https://github.com/aws-cloudformation/aws-cloudformation-rpdk-java-plugin/
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rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
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rpm_sourcerpm: python-cloudformation-cli-java-plugin-2.0.14-1.src.rpm
checksum_type: sha256
description:
  If you are using this package to build resource providers for CloudFormation, install the [CloudFormation CLI Java Plugin](https://github.com/aws-cloudformation/cloudformation-cli-java-plugin) - this will automatically install the the [CloudFormation CLI](https://github.com/aws-cloudformation/cloudformation-cli)! A Python virtual environment is recommended.
  **Prerequisites**
   - Python version 3.6 or above
   - Your choice of Java IDE
   - Lombok: The code generated by the CloudFormation CLI uses [Lombok](https://projectlombok.org/), which requires support in IDEs for some syntax highlighting to work. For the best development experience, it is recommended you install Lombok support in your IDE.
  **Installation**
  ```shell
  pip3 install cloudformation-cli-java-plugin
  ```
  Refer to the [CloudFormation CLI User Guide](https://docs.aws.amazon.com/cloudformation-cli/latest/userguide/resource-types.html) for the [CloudFormation CLI](https://github.com/aws-cloudformation/cloudformation-cli) for usage instructions.

  The `format` keyword can be specified on primitive types defined in a resource provider's schema to allow the CloudFormation CLI Java Plugin to generate more than the defaults for primitive types. Consult the table below for what formats are available and defaults for various types. The `default` value is used if omitted:
  | JSON Schema Type | Format value | Generated variable type  |
  | ---- | ----------- | ---------------------- |
  | boolean | `default` | [Boolean](https://docs.oracle.com/javase/8/docs/api/java/lang/Boolean.html)|
  | integer | `default`, `int32` | [Integer](https://docs.oracle.com/javase/8/docs/api/java/lang/Integer.html)|
  | integer | `int64` | [Long](https://docs.oracle.com/javase/8/docs/api/java/lang/Long.html)|
  | number | `default` | [Double](https://docs.oracle.com/javase/8/docs/api/java/lang/Double.html)|
  | string | `default` | [String](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html)|
  For example, the below schema for a property would generate a variable of type `Long`.
  ```
  {
      "type": "integer",
      "format": "int64"
  }
  ```

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version: 1.0.2
epoch: 0
release: 1
summary: Typescript language support for the CloudFormation CLI
url: https://github.com/aws-cloudformation/cloudformation-cli-typescript-plugin
rpm_license: Apache License 2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-dde1f8f9-321c-45b2-8cf0-c9c09503bea7
rpm_sourcerpm: python-cloudformation-cli-typescript-plugin-1.0.2-1.src.rpm
checksum_type: sha256
description:
  If you are using this package to build resource providers for CloudFormation, install the [CloudFormation CLI TypeScript Plugin](https://github.com/aws-cloudformation/cloudformation-cli-typescript-plugin) - this will automatically install the [CloudFormation CLI](https://github.com/aws-cloudformation/cloudformation-cli)! A Python virtual environment is recommended.
  **Prerequisites**
  - Python version 3.6 or above
  - [SAM CLI](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-sam-cli-install.html)
  - Your choice of TypeScript IDE
  **Installation**
  ```shell
  pip3 install cloudformation-cli-typescript-plugin
  ```
  Refer to the [CloudFormation CLI User Guide](https://docs.aws.amazon.com/cloudformation-cli/latest/userguide/resource-types.html) for the [CloudFormation CLI](https://github.com/aws-cloudformation/cloudformation-cli) for usage instructions.
  **Howto**
  Example run:
  ```
  $ cfn init
  Initializing new project
  What's the name of your resource type?
  (Organization::Service::Resource)
  >> Foo::Bar::Baz
  Select a language for code generation:
  [1] java
  [2] typescript
  (enter an integer):
  >> 2
  Use docker for platform-independent packaging (Y/n)?
  This is highly recommended unless you are experienced
  with cross-platform Typescript packaging.
  >> y
  Initialized a new project in <>
  $ cfn submit --dry-run
  $ sam local invoke --event sam-tests/create.json TestEntrypoint
  ```

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version: 1.4.2
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checksum_type: sha256
description:
  [![image](https://badge.fury.io/py/django-better-admin-arrayfield.svg)](https://badge.fury.io/py/django-better-admin-arrayfield)

  [![Actions Status](https://github.com/gradam/django-better-admin-arrayfield/workflows/tests/badge.svg)](https://github.com/gradam/django-better-admin-arrayfield/actions)

  [![image](https://codecov.io/gh/gradam/django-better-admin-arrayfield/branch/master/graph/badge.svg)](https://codecov.io/gh/gradam/django-better-admin-arrayfield)

  Better ArrayField widget for admin

  Supported Python versions: [![Python 3.5](https://img.shields.io/badge/python-3.5-blue.svg)](https://www.python.org/downloads/release/python-350/) [![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/) [![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/) [![Python 3.8](https://img.shields.io/badge/python-3.8-blue.svg)](https://www.python.org/downloads/release/python-380/)





  Supported Django versions: 2.0, 2.1, 2.2, 3.0, 3.1

  might work with different django/python versions as well but I did not test that.

  It changes comma separated widget to list based in admin panel.

  Before:
  ![Alt text](https://raw.githubusercontent.com/gradam/django-better-admin-arrayfield/master/readme_images/before.jpg "Before")

  After:
  ![Alt text](https://raw.githubusercontent.com/gradam/django-better-admin-arrayfield/master/readme_images/after.png "After")



  Install Django better admin ArrayField:

      pip install django-better-admin-arrayfield

  Add it to your \`INSTALLED\_APPS\`:

  ```python
  INSTALLED_APPS = (
      ...
      'django_better_admin_arrayfield',
      ...
  )
  ```




  `django_better_admin_arrayfield.models.fields.ArrayField` is a drop-in replacement for standard Django `ArrayField`.

  Import it like below and use it in your model class definition.
  ```python
  from django_better_admin_arrayfield.models.fields import ArrayField
  ```

  Import DynamicArrayMixin like below
  ```python
  from django_better_admin_arrayfield.admin.mixins import DynamicArrayMixin
  ```

  In your admin class add `DynamicArrayMixin`:
      ...
  ```python
  class MyModelAdmin(admin.ModelAdmin, DynamicArrayMixin):
  ```

  That's it.




  By default the subwidget (the one used for each item in the array) will be TextInput. If you want something else, you can use your own specifying it in the `formfield_overrides` of your Admin model:
  ```python
  class MyWidget(DynamicArrayWidget):
      def __init__(self, *args, **kwargs):
          kwargs['subwidget_form'] = MyForm
          super().__init__(*args, **kwargs)

  class MyModelAdmin(models.ModelAdmin, DynamicArrayMixin):
      ...
      formfield_overrides = {
          DynamicArrayField: {'widget': MyWidget},
      }
  ```

  If you wanted to have Textarea as the subwidget, you can simply use the included drop-in widget replacement:
  ```python
  from django_better_admin_arrayfield.forms.widgets import DynamicArrayTextareaWidget

  class MyModelAdmin(models.ModelAdmin, DynamicArrayMixin):
      ...
      formfield_overrides = {
          DynamicArrayField: {'widget': DynamicArrayTextareaWidget},
      }
  ```



  Does the code actually work?

      source <YOURVIRTUALENV>/bin/activate
      (myenv) $ pip install tox
      (myenv) $ tox



  Install pre-commit black hook

      source <YOURVIRTUALENV>/bin/activate
      (myenv) $ pip install -r requirements_dev.txt
      (myenv) $ pre-commit install



  Inspired by: https://stackoverflow.com/a/49370480/4638248

  Tools used in rendering this
      package:

    - [Cookiecutter](https://github.com/audreyr/cookiecutter)
    - [cookiecutter-djangopackage](https://github.com/pydanny/cookiecutter-djangopackage)






    - Adjust template to better match django style



    - Allow submitting empty array field



    - allow choosing subwidget for DynamicArrayWidget



    - Handle default values in form field



    - Fix tests requirements



    - handle default values in model fields



    - Add spanish translations



    - Add possibility to i18n strings



    - Remove debugging print statements
    - use default_app_config for easier integration
    - Support dynamically-added inline forms



    - Add python 3.8 and Django 3.0 to tests



    - Can add item after removing everything from the list



    - Can add item after removing everything from the list
    - Do not call static at startup time



    - If field is required empty list raises ValidationError on clean.



    - Empty list is no longer recognized as changed.




    - First release on PyPI.

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release: 1
summary: Google Endpoints API management
url: https://github.com/cloudendpoints/endpoints-management-python
rpm_license: Apache License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-94414c61-64aa-4afa-8916-6e30b0ac886f
rpm_sourcerpm: python-google-endpoints-api-management-1.11.1-1.src.rpm
checksum_type: sha256
description:
  Google Endpoints API Management manages the 'control plane' of an API by
  providing support for authentication, billing, monitoring and quota control.
  It achieves this by
  - allowing HTTP servers to control access to their APIs using the Google Service Management and Google Service Control APIs
  - providing built-in, standards-compliant support for third-party authentication
  - doing this with minimal performance impact via the use of advanced caching and aggregation algorithms
  - making this easy to integrate via a set of `WSGI`_ middleware
  Example:
     >>> application = MyWsgiApp()  # an existing WSGI application
     >>>
     >>> # the name of the controlled service
     >>> service_name = 'my-service-name'
     >>>
     >>> # The Id of a Google Cloud project with the Service Control and Service Management
     >>> # APIs enabled
     >>> project_id = 'my-project-id'
     >>>
     >>> # wrap the app for service control
     >>> from endpoints_management.control import client, wsgi
     >>> control_client = client.Loaders.DEFAULT.load(service_name)
     >>> control_client.start()
     >>> controlled_app = wsgi.add_all(application, project_id, control_client)
     >>>
     >>> # now use the controlled in place of application
     >>> my_server.serve(controlled_app)

location_href:
  00011168-python-google-endpoints-api-management/python3-google-endpoints-api-management-1.11.1-1.noarch.rpm


pkgId: 3c0443316f2e482d70c2634b0efe6b73ef9d7790144ed743068629df25526717
name: python3-jupyter_highlight_selected_word
arch: noarch
version: 0.2.0
epoch: 0
release: 1
url: https://github.com/jcb91/jupyter_highlight_selected_word.git
rpm_license: BSD
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-cb4311d5-160a-4379-87a3-3bf527605c63
rpm_sourcerpm: python-jupyter_highlight_selected_word-0.2.0-1.src.rpm
checksum_type: sha256
summary:
  Jupyter notebook extension that enables highlighting every instance of the current word in the notebook.

description:
  Jupyter notebook extension that enables highlighting of all instances of the
  currently-selected or cursor-adjecent word in either the current cell's editor,
  or in the whole notebook.
  Based on the  CodeMirror addon
  `Match Highlighter <https://codemirror.net/demo/matchhighlighter.html>`_,
  extended to work across multiple editors.

location_href:
  00010397-python-jupyter-highlight-selected-word/python3-jupyter_highlight_selected_word-0.2.0-1.noarch.rpm


pkgId: e06d0f3319c0ea15570b7529cbe8d5d3dfb83fdb98c1181cbde01bd8a44ca3fe
name: python3-keras-position-wise-feed-forward
arch: noarch
version: 0.8.0
epoch: 0
release: 1
summary: Feed forward layer implemented in Keras
url: https://github.com/CyberZHG/keras-position-wise-feed-forward
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-209e3bbe-bbb7-4abf-8062-310e49e502a0
rpm_sourcerpm: python-keras-position-wise-feed-forward-0.8.0-1.src.rpm
checksum_type: sha256
description:
  [![Version](https://img.shields.io/pypi/v/keras-position-wise-feed-forward.svg)](https://pypi.org/project/keras-position-wise-feed-forward/)
  ![License](https://img.shields.io/pypi/l/keras-position-wise-feed-forward.svg)

  Implementation of position-wise feed forward layer in the paper: [Attention is All You Need](https://arxiv.org/pdf/1706.03762.pdf)



  ```bash
  pip install keras-position-wise-feed-forward
  ```



  ```python
  from tensorflow import keras
  from keras_position_wise_feed_forward import FeedForward

  input_layer = keras.layers.Input(shape=(None, 32))
  feed_forward_layer = FeedForward(units=128)(input_layer)
  model = keras.models.Model(inputs=input_layer, outputs=feed_forward_layer)
  model.compile(optimizer='adam', loss='mse')
  model.summary()
  ```

location_href:
  00013940-python-keras-position-wise-feed-forward/python3-keras-position-wise-feed-forward-0.8.0-1.noarch.rpm


pkgId: 2887ed221aa9f682555f1a5e71e6c4c96368f1d8d21feb4fd6e74e99d48b4d68
name: python3-magic-wormhole-mailbox-server
arch: noarch
version: 0.4.1
epoch: 0
release: 1
summary: Securely transfer data between computers
url: https://github.com/warner/magic-wormhole-mailbox-server
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-7a99d0a2-b46a-4bad-b2f2-f640f7ae0fb0
rpm_sourcerpm: python-magic-wormhole-mailbox-server-0.4.1-1.src.rpm
checksum_type: sha256
description:
[cell repeats an earlier one in this file: 3359 characters]

location_href:
  00004142-python-magic-wormhole-mailbox-server/python3-magic-wormhole-mailbox-server-0.4.1-1.noarch.rpm


pkgId: 69abc8aa90e5a4d3cbe8988bffbbf5e38131c0ebaa06eab74da83837df4e20d7
name: python3-magic-wormhole-mailbox-server
arch: noarch
version: 0.4.1
epoch: 0
release: 1
summary: Securely transfer data between computers
url: https://github.com/warner/magic-wormhole-mailbox-server
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-4f5c87d6-9b7d-462d-95c0-cd025bd743a6
rpm_sourcerpm: python-magic-wormhole-mailbox-server-0.4.1-1.src.rpm
checksum_type: sha256
description:
[cell repeats an earlier one in this file: 3359 characters]

location_href:
  00007486-python-magic-wormhole-mailbox-server/python3-magic-wormhole-mailbox-server-0.4.1-1.noarch.rpm


pkgId: 724a0ae19ffbb1bdff2a2f1f29a94b1f977485e21f40d9f0d5c15a5e5e4970c5
name: python3-magic-wormhole-transit-relay
arch: noarch
version: 0.2.1
epoch: 0
release: 1
summary: Transit Relay server for Magic-Wormhole
url: https://github.com/warner/magic-wormhole-transit-relay
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-3e7fab77-6acc-4229-a15c-9af5fc20614a
rpm_sourcerpm: python-magic-wormhole-transit-relay-0.2.1-1.src.rpm
checksum_type: sha256
location_href:
  00004143-python-magic-wormhole-transit-relay/python3-magic-wormhole-transit-relay-0.2.1-1.noarch.rpm


pkgId: b4571dbe845516520e77f413f49b15a8ebb6666e8df4fd833e3e193fd08eae25
name: python3-magic-wormhole-transit-relay
arch: noarch
version: 0.2.1
epoch: 0
release: 1
summary: Transit Relay server for Magic-Wormhole
url: https://github.com/warner/magic-wormhole-transit-relay
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-67e141b2-4f01-4ff3-99a9-b028c914947d
rpm_sourcerpm: python-magic-wormhole-transit-relay-0.2.1-1.src.rpm
checksum_type: sha256
location_href:
  00007487-python-magic-wormhole-transit-relay/python3-magic-wormhole-transit-relay-0.2.1-1.noarch.rpm


pkgId: 8cb13a37bf8d046fbab09d9c15708cf4f362ea9324010b4ff1d1c80149c12c3e
name: python3-mypy-boto3-elasticbeanstalk
arch: noarch
version: 1.26.59
epoch: 0
release: 1
url: https://github.com/youtype/mypy_boto3_builder
rpm_license: MIT License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-983385bb-34e6-4ae0-a11a-6c6ff9b27d58
rpm_sourcerpm: python-mypy-boto3-elasticbeanstalk-1.26.59-1.src.rpm
checksum_type: sha256
summary:
  Type annotations for boto3.ElasticBeanstalk 1.26.59 service generated with mypy-boto3-builder 7.12.3

description:
  <a id="mypy-boto3-elasticbeanstalk"></a>



  [![PyPI - mypy-boto3-elasticbeanstalk](https://img.shields.io/pypi/v/mypy-boto3-elasticbeanstalk.svg?color=blue)](https://pypi.org/project/mypy-boto3-elasticbeanstalk)
  [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mypy-boto3-elasticbeanstalk.svg?color=blue)](https://pypi.org/project/mypy-boto3-elasticbeanstalk)
  [![Docs](https://img.shields.io/readthedocs/mypy-boto3-builder.svg?color=blue)](https://mypy-boto3-builder.readthedocs.io/)
  [![PyPI - Downloads](https://img.shields.io/pypi/dm/mypy-boto3-elasticbeanstalk?color=blue)](https://pypistats.org/packages/mypy-boto3-elasticbeanstalk)

  ![boto3.typed](https://github.com/youtype/mypy_boto3_builder/raw/main/logo.png)

  Type annotations for
  [boto3.ElasticBeanstalk 1.26.59](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/elasticbeanstalk.html#ElasticBeanstalk)
  service compatible with [VSCode](https://code.visualstudio.com/),
  [PyCharm](https://www.jetbrains.com/pycharm/),
  [Emacs](https://www.gnu.org/software/emacs/),
  [Sublime Text](https://www.sublimetext.com/),
  [mypy](https://github.com/python/mypy),
  [pyright](https://github.com/microsoft/pyright) and other tools.

  Generated by
  [mypy-boto3-builder 7.12.3](https://github.com/youtype/mypy_boto3_builder).

  More information can be found on
  [boto3-stubs](https://pypi.org/project/boto3-stubs/) page and in
  [mypy-boto3-elasticbeanstalk docs](https://youtype.github.io/boto3_stubs_docs/mypy_boto3_elasticbeanstalk/).

  See how it helps to find and fix potential bugs:

  ![boto3-stubs demo](https://github.com/youtype/mypy_boto3_builder/raw/main/demo.gif)

  - [mypy-boto3-elasticbeanstalk](#mypy-boto3-elasticbeanstalk)
    - [How to install](#how-to-install)
      - [VSCode extension](#vscode-extension)
      - [From PyPI with pip](#from-pypi-with-pip)
    - [How to uninstall](#how-to-uninstall)
    - [Usage](#usage)
      - [VSCode](#vscode)
      - [PyCharm](#pycharm)
      - [Emacs](#emacs)
      - [Sublime Text](#sublime-text)
      - [Other IDEs](#other-ides)
      - [mypy](#mypy)
      - [pyright](#pyright)
    - [Explicit type annotations](#explicit-type-annotations)
      - [Client annotations](#client-annotations)
      - [Paginators annotations](#paginators-annotations)
      - [Waiters annotations](#waiters-annotations)
      - [Literals](#literals)
      - [Typed dictionaries](#typed-dictionaries)
    - [How it works](#how-it-works)
    - [What's new](#what's-new)
      - [Implemented features](#implemented-features)
      - [Latest changes](#latest-changes)
    - [Versioning](#versioning)
    - [Thank you](#thank-you)
    - [Documentation](#documentation)
    - [Support and contributing](#support-and-contributing)

  <a id="how-to-install"></a>



  <a id="vscode-extension"></a>



  Add
  [AWS Boto3](https://marketplace.visualstudio.com/items?itemName=Boto3typed.boto3-ide)
  extension to your VSCode and run `AWS boto3: Quick Start` command.

  Click `Modify` and select `boto3 common` and `ElasticBeanstalk`.

  <a id="from-pypi-with-pip"></a>



  Install `boto3-stubs` for `ElasticBeanstalk` service.

  ```bash

  python -m pip install 'boto3-stubs[elasticbeanstalk]'




  python -m pip install 'boto3-stubs-lite[elasticbeanstalk]'



  python -m pip install mypy-boto3-elasticbeanstalk
  ```

  <a id="how-to-uninstall"></a>



  ```bash
  python -m pip uninstall -y mypy-boto3-elasticbeanstalk
  ```

  <a id="usage"></a>



  <a id="vscode"></a>



  - Install
    [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
  - Install
    [Pylance extension](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
  - Set `Pylance` as your Python Language Server
  - Install `boto3-stubs[elasticbeanstalk]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[elasticbeanstalk]'
  ```

  Both type checking and code completion should now work. No explicit type
  annotations required, write your `boto3` code as usual.

  <a id="pycharm"></a>



  Install `boto3-stubs-lite[elasticbeanstalk]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs-lite[elasticbeanstalk]'`
  ```

  Both type checking and code completion should now work. Explicit type
  annotations **are required**.

  Use `boto3-stubs` package instead for implicit type discovery.

  <a id="emacs"></a>



  - Install `boto3-stubs` with services you use in your environment:

  ```bash
  python -m pip install 'boto3-stubs[elasticbeanstalk]'
  ```

  - Install [use-package](https://github.com/jwiegley/use-package),
    [lsp](https://github.com/emacs-lsp/lsp-mode/),
    [company](https://github.com/company-mode/company-mode) and
    [flycheck](https://github.com/flycheck/flycheck) packages
  - Install [lsp-pyright](https://github.com/emacs-lsp/lsp-pyright) package

  ```elisp
  (use-package lsp-pyright
    :ensure t
    :hook (python-mode . (lambda ()
                            (require 'lsp-pyright)
                            (lsp)))  ; or lsp-deferred
    :init (when (executable-find "python3")
            (setq lsp-pyright-python-executable-cmd "python3"))
    )
  ```

  - Make sure emacs uses the environment where you have installed `boto3-stubs`

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="sublime-text"></a>



  - Install `boto3-stubs[elasticbeanstalk]` with services you use in your
    environment:

  ```bash
  python -m pip install 'boto3-stubs[elasticbeanstalk]'
  ```

  - Install [LSP-pyright](https://github.com/sublimelsp/LSP-pyright) package

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="other-ides"></a>



  Not tested, but as long as your IDE supports `mypy` or `pyright`, everything
  should work.

  <a id="mypy"></a>



  - Install `mypy`: `python -m pip install mypy`
  - Install `boto3-stubs[elasticbeanstalk]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[elasticbeanstalk]'`
  ```

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="pyright"></a>



  - Install `pyright`: `npm i -g pyright`
  - Install `boto3-stubs[elasticbeanstalk]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[elasticbeanstalk]'
  ```

  Optionally, you can install `boto3-stubs` to `typings` folder.

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="explicit-type-annotations"></a>



  <a id="client-annotations"></a>



  `ElasticBeanstalkClient` provides annotations for
  `boto3.client("elasticbeanstalk")`.

  ```python
  from boto3.session import Session

  from mypy_boto3_elasticbeanstalk import ElasticBeanstalkClient

  client: ElasticBeanstalkClient = Session().client("elasticbeanstalk")


  ```

  <a id="paginators-annotations"></a>



  `mypy_boto3_elasticbeanstalk.paginator` module contains type annotations for
  all paginators.

  ```python
  from boto3.session import Session

  from mypy_boto3_elasticbeanstalk import ElasticBeanstalkClient
  from mypy_boto3_elasticbeanstalk.paginator import (
      DescribeApplicationVersionsPaginator,
      DescribeEnvironmentManagedActionHistoryPaginator,
      DescribeEnvironmentsPaginator,
      DescribeEventsPaginator,
      ListPlatformVersionsPaginator,
  )

  client: ElasticBeanstalkClient = Session().client("elasticbeanstalk")



  describe_application_versions_paginator: DescribeApplicationVersionsPaginator = (
      client.get_paginator("describe_application_versions")
  )
  describe_environment_managed_action_history_paginator: DescribeEnvironmentManagedActionHistoryPaginator = client.get_paginator(
      "describe_environment_managed_action_history"
  )
  describe_environments_paginator: DescribeEnvironmentsPaginator = client.get_paginator(
      "describe_environments"
  )
  describe_events_paginator: DescribeEventsPaginator = client.get_paginator("describe_events")
  list_platform_versions_paginator: ListPlatformVersionsPaginator = client.get_paginator(
      "list_platform_versions"
  )
  ```

  <a id="waiters-annotations"></a>



  `mypy_boto3_elasticbeanstalk.waiter` module contains type annotations for all
  waiters.

  ```python
  from boto3.session import Session

  from mypy_boto3_elasticbeanstalk import ElasticBeanstalkClient
  from mypy_boto3_elasticbeanstalk.waiter import (
      EnvironmentExistsWaiter,
      EnvironmentTerminatedWaiter,
      EnvironmentUpdatedWaiter,
  )

  client: ElasticBeanstalkClient = Session().client("elasticbeanstalk")



  environment_exists_waiter: EnvironmentExistsWaiter = client.get_waiter("environment_exists")
  environment_terminated_waiter: EnvironmentTerminatedWaiter = client.get_waiter(
      "environment_terminated"
  )
  environment_updated_waiter: EnvironmentUpdatedWaiter = client.get_waiter("environment_updated")
  ```

  <a id="literals"></a>



  `mypy_boto3_elasticbeanstalk.literals` module contains literals extracted from
  shapes that can be used in user code for type checking.

  ```python
  from mypy_boto3_elasticbeanstalk.literals import (
      ActionHistoryStatusType,
      ActionStatusType,
      ActionTypeType,
      ApplicationVersionStatusType,
      ComputeTypeType,
      ConfigurationDeploymentStatusType,
      ConfigurationOptionValueTypeType,
      DescribeApplicationVersionsPaginatorName,
      DescribeEnvironmentManagedActionHistoryPaginatorName,
      DescribeEnvironmentsPaginatorName,
      DescribeEventsPaginatorName,
      EnvironmentExistsWaiterName,
      EnvironmentHealthAttributeType,
      EnvironmentHealthStatusType,
      EnvironmentHealthType,
      EnvironmentInfoTypeType,
      EnvironmentStatusType,
      EnvironmentTerminatedWaiterName,
      EnvironmentUpdatedWaiterName,
      EventSeverityType,
      FailureTypeType,
      InstancesHealthAttributeType,
      ListPlatformVersionsPaginatorName,
      PlatformStatusType,
      SourceRepositoryType,
      SourceTypeType,
      ValidationSeverityType,
      ElasticBeanstalkServiceName,
      ServiceName,
      ResourceServiceName,
      PaginatorName,
      WaiterName,
      RegionName,
  )


  def check_value(value: ActionHistoryStatusType) -> bool:
      ...
  ```

  <a id="typed-dictionaries"></a>



  `mypy_boto3_elasticbeanstalk.type_defs` module contains structures and shapes
  assembled to typed dictionaries for additional type checking.

  ```python
  from mypy_boto3_elasticbeanstalk.type_defs import (
      AbortEnvironmentUpdateMessageRequestTypeDef,
      ResponseMetadataTypeDef,
      LatencyTypeDef,
      StatusCodesTypeDef,
      S3LocationTypeDef,
      SourceBuildInformationTypeDef,
      MaxAgeRuleTypeDef,
      MaxCountRuleTypeDef,
      ApplyEnvironmentManagedActionRequestRequestTypeDef,
      AssociateEnvironmentOperationsRoleMessageRequestTypeDef,
      AutoScalingGroupTypeDef,
      BuildConfigurationTypeDef,
      BuilderTypeDef,
      CPUUtilizationTypeDef,
      CheckDNSAvailabilityMessageRequestTypeDef,
      ComposeEnvironmentsMessageRequestTypeDef,
      OptionRestrictionRegexTypeDef,
      ConfigurationOptionSettingTypeDef,
      ValidationMessageTypeDef,
      TagTypeDef,
      SourceConfigurationTypeDef,
      EnvironmentTierTypeDef,
      OptionSpecificationTypeDef,
      PlatformSummaryTypeDef,
      CustomAmiTypeDef,
      DeleteApplicationMessageRequestTypeDef,
      DeleteApplicationVersionMessageRequestTypeDef,
      DeleteConfigurationTemplateMessageRequestTypeDef,
      DeleteEnvironmentConfigurationMessageRequestTypeDef,
      DeletePlatformVersionRequestRequestTypeDef,
      DeploymentTypeDef,
      PaginatorConfigTypeDef,
      DescribeApplicationVersionsMessageRequestTypeDef,
      DescribeApplicationsMessageRequestTypeDef,
      DescribeConfigurationSettingsMessageRequestTypeDef,
      DescribeEnvironmentHealthRequestRequestTypeDef,
      InstanceHealthSummaryTypeDef,
      DescribeEnvironmentManagedActionHistoryRequestRequestTypeDef,
      ManagedActionHistoryItemTypeDef,
      DescribeEnvironmentManagedActionsRequestRequestTypeDef,
      ManagedActionTypeDef,
      DescribeEnvironmentResourcesMessageRequestTypeDef,
      WaiterConfigTypeDef,
      DescribeEnvironmentsMessageRequestTypeDef,
      DescribeEventsMessageRequestTypeDef,
      DescribeInstancesHealthRequestRequestTypeDef,
      DescribePlatformVersionRequestRequestTypeDef,
      DisassociateEnvironmentOperationsRoleMessageRequestTypeDef,
      EnvironmentLinkTypeDef,
      EnvironmentInfoDescriptionTypeDef,
      InstanceTypeDef,
      LaunchConfigurationTypeDef,
      LaunchTemplateTypeDef,
      LoadBalancerTypeDef,
      QueueTypeDef,
      TriggerTypeDef,
      EventDescriptionTypeDef,
      SolutionStackDescriptionTypeDef,
      SearchFilterTypeDef,
      PlatformBranchSummaryTypeDef,
      PlatformFilterTypeDef,
      ListTagsForResourceMessageRequestTypeDef,
      ListenerTypeDef,
      PlatformFrameworkTypeDef,
      PlatformProgrammingLanguageTypeDef,
      RebuildEnvironmentMessageRequestTypeDef,
      RequestEnvironmentInfoMessageRequestTypeDef,
      ResourceQuotaTypeDef,
      RestartAppServerMessageRequestTypeDef,
      RetrieveEnvironmentInfoMessageRequestTypeDef,
      SwapEnvironmentCNAMEsMessageRequestTypeDef,
      TerminateEnvironmentMessageRequestTypeDef,
      UpdateApplicationMessageRequestTypeDef,
      UpdateApplicationVersionMessageRequestTypeDef,
      ApplyEnvironmentManagedActionResultTypeDef,
      CheckDNSAvailabilityResultMessageTypeDef,
      CreateStorageLocationResultMessageTypeDef,
      EmptyResponseMetadataTypeDef,
      ApplicationMetricsTypeDef,
      ApplicationVersionDescriptionTypeDef,
      ApplicationVersionLifecycleConfigTypeDef,
      SystemStatusTypeDef,
      ConfigurationOptionDescriptionTypeDef,
      ConfigurationSettingsDescriptionResponseMetadataTypeDef,
      ConfigurationSettingsDescriptionTypeDef,
      ValidateConfigurationSettingsMessageRequestTypeDef,
      ConfigurationSettingsValidationMessagesTypeDef,
      CreateApplicationVersionMessageRequestTypeDef,
      CreatePlatformVersionRequestRequestTypeDef,
      ResourceTagsDescriptionMessageTypeDef,
      UpdateTagsForResourceMessageRequestTypeDef,
      CreateConfigurationTemplateMessageRequestTypeDef,
      CreateEnvironmentMessageRequestTypeDef,
      DescribeConfigurationOptionsMessageRequestTypeDef,
      UpdateConfigurationTemplateMessageRequestTypeDef,
      UpdateEnvironmentMessageRequestTypeDef,
      CreatePlatformVersionResultTypeDef,
      DeletePlatformVersionResultTypeDef,
      ListPlatformVersionsResultTypeDef,
      DescribeApplicationVersionsMessageDescribeApplicationVersionsPaginateTypeDef,
      DescribeEnvironmentManagedActionHistoryRequestDescribeEnvironmentManagedActionHistoryPaginateTypeDef,
      DescribeEnvironmentsMessageDescribeEnvironmentsPaginateTypeDef,
      DescribeEventsMessageDescribeEventsPaginateTypeDef,
      DescribeEnvironmentManagedActionHistoryResultTypeDef,
      DescribeEnvironmentManagedActionsResultTypeDef,
      DescribeEnvironmentsMessageEnvironmentExistsWaitTypeDef,
      DescribeEnvironmentsMessageEnvironmentTerminatedWaitTypeDef,
      DescribeEnvironmentsMessageEnvironmentUpdatedWaitTypeDef,
      RetrieveEnvironmentInfoResultMessageTypeDef,
      EnvironmentResourceDescriptionTypeDef,
      EventDescriptionsMessageTypeDef,
      ListAvailableSolutionStacksResultMessageTypeDef,
      ListPlatformBranchesRequestRequestTypeDef,
      ListPlatformBranchesResultTypeDef,
      ListPlatformVersionsRequestListPlatformVersionsPaginateTypeDef,
      ListPlatformVersionsRequestRequestTypeDef,
      LoadBalancerDescriptionTypeDef,
      PlatformDescriptionTypeDef,
      ResourceQuotasTypeDef,
      DescribeEnvironmentHealthResultTypeDef,
      ApplicationVersionDescriptionMessageTypeDef,
      ApplicationVersionDescriptionsMessageTypeDef,
      ApplicationResourceLifecycleConfigTypeDef,
      SingleInstanceHealthTypeDef,
      ConfigurationOptionsDescriptionTypeDef,
      ConfigurationSettingsDescriptionsTypeDef,
      EnvironmentResourceDescriptionsMessageTypeDef,
      EnvironmentResourcesDescriptionTypeDef,
      DescribePlatformVersionResultTypeDef,
      DescribeAccountAttributesResultTypeDef,
      ApplicationDescriptionTypeDef,
      ApplicationResourceLifecycleDescriptionMessageTypeDef,
      CreateApplicationMessageRequestTypeDef,
      UpdateApplicationResourceLifecycleMessageRequestTypeDef,
      DescribeInstancesHealthResultTypeDef,
      EnvironmentDescriptionResponseMetadataTypeDef,
      EnvironmentDescriptionTypeDef,
      ApplicationDescriptionMessageTypeDef,
      ApplicationDescriptionsMessageTypeDef,
      EnvironmentDescriptionsMessageTypeDef,
  )


  def get_structure() -> AbortEnvironmentUpdateMessageRequestTypeDef:
      return {...}
  ```

  <a id="how-it-works"></a>



  Fully automated
  [mypy-boto3-builder](https://github.com/youtype/mypy_boto3_builder) carefully
  generates type annotations for each service, patiently waiting for `boto3`
  updates. It delivers a drop-in type annotations for you and makes sure that:

  - All available `boto3` services are covered.
  - Each public class and method of every `boto3` service gets valid type
    annotations extracted from the documentation (blame `botocore` docs if types
    are incorrect).
  - Type annotations include up-to-date documentation.
  - Link to documentation is provided for every method.
  - Code is processed by [black](https://github.com/psf/black) and
    [isort](https://github.com/PyCQA/isort) for readability.

  <a id="what's-new"></a>



  <a id="implemented-features"></a>



  - Fully type annotated `boto3`, `botocore` and `aiobotocore` libraries
  - `mypy`, `pyright`, `VSCode`, `PyCharm`, `Sublime Text` and `Emacs`
    compatibility
  - `Client`, `ServiceResource`, `Resource`, `Waiter` `Paginator` type
    annotations for each service
  - Generated `TypeDefs` for each service
  - Generated `Literals` for each service
  - Auto discovery of types for `boto3.client` and `boto3.session` calls
  - Auto discovery of types for `session.client` and `session.session` calls
  - Auto discovery of types for `client.get_waiter` and `client.get_paginator`
    calls
  - Auto discovery of types for `ServiceResource` and `Resource` collections
  - Auto discovery of types for `aiobotocore.Session.create_client` calls

  <a id="latest-changes"></a>



  Builder changelog can be found in
  [Releases](https://github.com/youtype/mypy_boto3_builder/releases).

  <a id="versioning"></a>



  `mypy-boto3-elasticbeanstalk` version is the same as related `boto3` version
  and follows [PEP 440](https://www.python.org/dev/peps/pep-0440/) format.

  <a id="thank-you"></a>



  - [Allie Fitter](https://github.com/alliefitter) for
    [boto3-type-annotations](https://pypi.org/project/boto3-type-annotations/),
    this package is based on top of his work
  - [black](https://github.com/psf/black) developers for an awesome formatting
    tool
  - [Timothy Edmund Crosley](https://github.com/timothycrosley) for
    [isort](https://github.com/PyCQA/isort) and how flexible it is
  - [mypy](https://github.com/python/mypy) developers for doing all dirty work
    for us
  - [pyright](https://github.com/microsoft/pyright) team for the new era of typed
    Python

  <a id="documentation"></a>



  All services type annotations can be found in
  [boto3 docs](https://youtype.github.io/boto3_stubs_docs/mypy_boto3_elasticbeanstalk/)

  <a id="support-and-contributing"></a>



  This package is auto-generated. Please reports any bugs or request new features
  in [mypy-boto3-builder](https://github.com/youtype/mypy_boto3_builder/issues/)
  repository.

location_href:
  00014240-python-mypy-boto3-elasticbeanstalk/python3-mypy-boto3-elasticbeanstalk-1.26.59-1.noarch.rpm


pkgId: 37c9298de3f89acfba3160d3fc725c9a3e5e93eb30cf0f95bd1da36f6980cf83
name: python3-mypy-boto3-marketplace-entitlement
arch: noarch
version: 1.26.0.post1
epoch: 0
release: 1
url: https://github.com/youtype/mypy_boto3_builder
rpm_license: MIT License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-370869ab-f3ed-477f-abcc-83200424413f
rpm_sourcerpm: python-mypy-boto3-marketplace-entitlement-1.26.0.post1-1.src.rpm
checksum_type: sha256
summary:
  Type annotations for boto3.MarketplaceEntitlementService 1.26.0 service generated with mypy-boto3-builder 7.11.10

description:
  <a id="mypy-boto3-marketplace-entitlement"></a>



  [![PyPI - mypy-boto3-marketplace-entitlement](https://img.shields.io/pypi/v/mypy-boto3-marketplace-entitlement.svg?color=blue)](https://pypi.org/project/mypy-boto3-marketplace-entitlement)
  [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mypy-boto3-marketplace-entitlement.svg?color=blue)](https://pypi.org/project/mypy-boto3-marketplace-entitlement)
  [![Docs](https://img.shields.io/readthedocs/mypy-boto3-builder.svg?color=blue)](https://mypy-boto3-builder.readthedocs.io/)
  [![PyPI - Downloads](https://img.shields.io/pypi/dm/mypy-boto3-marketplace-entitlement?color=blue)](https://pypistats.org/packages/mypy-boto3-marketplace-entitlement)

  ![boto3.typed](https://github.com/youtype/mypy_boto3_builder/raw/main/logo.png)

  Type annotations for
  [boto3.MarketplaceEntitlementService 1.26.0](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/marketplace-entitlement.html#MarketplaceEntitlementService)
  service compatible with [VSCode](https://code.visualstudio.com/),
  [PyCharm](https://www.jetbrains.com/pycharm/),
  [Emacs](https://www.gnu.org/software/emacs/),
  [Sublime Text](https://www.sublimetext.com/),
  [mypy](https://github.com/python/mypy),
  [pyright](https://github.com/microsoft/pyright) and other tools.

  Generated by
  [mypy-boto3-builder 7.11.10](https://github.com/youtype/mypy_boto3_builder).

  More information can be found on
  [boto3-stubs](https://pypi.org/project/boto3-stubs/) page and in
  [mypy-boto3-marketplace-entitlement docs](https://youtype.github.io/boto3_stubs_docs/mypy_boto3_marketplace_entitlement/).

  See how it helps to find and fix potential bugs:

  ![boto3-stubs demo](https://github.com/youtype/mypy_boto3_builder/raw/main/demo.gif)

  - [mypy-boto3-marketplace-entitlement](#mypy-boto3-marketplace-entitlement)
    - [How to install](#how-to-install)
      - [VSCode extension](#vscode-extension)
      - [From PyPI with pip](#from-pypi-with-pip)
    - [How to uninstall](#how-to-uninstall)
    - [Usage](#usage)
      - [VSCode](#vscode)
      - [PyCharm](#pycharm)
      - [Emacs](#emacs)
      - [Sublime Text](#sublime-text)
      - [Other IDEs](#other-ides)
      - [mypy](#mypy)
      - [pyright](#pyright)
    - [Explicit type annotations](#explicit-type-annotations)
      - [Client annotations](#client-annotations)
      - [Paginators annotations](#paginators-annotations)
      - [Literals](#literals)
      - [Typed dictionaries](#typed-dictionaries)
    - [How it works](#how-it-works)
    - [What's new](#what's-new)
      - [Implemented features](#implemented-features)
      - [Latest changes](#latest-changes)
    - [Versioning](#versioning)
    - [Thank you](#thank-you)
    - [Documentation](#documentation)
    - [Support and contributing](#support-and-contributing)

  <a id="how-to-install"></a>



  <a id="vscode-extension"></a>



  Add
  [AWS Boto3](https://marketplace.visualstudio.com/items?itemName=Boto3typed.boto3-ide)
  extension to your VSCode and run `AWS boto3: Quick Start` command.

  Click `Modify` and select `boto3 common` and `MarketplaceEntitlementService`.

  <a id="from-pypi-with-pip"></a>



  Install `boto3-stubs` for `MarketplaceEntitlementService` service.

  ```bash

  python -m pip install 'boto3-stubs[marketplace-entitlement]'




  python -m pip install 'boto3-stubs-lite[marketplace-entitlement]'



  python -m pip install mypy-boto3-marketplace-entitlement
  ```

  <a id="how-to-uninstall"></a>



  ```bash
  python -m pip uninstall -y mypy-boto3-marketplace-entitlement
  ```

  <a id="usage"></a>



  <a id="vscode"></a>



  - Install
    [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
  - Install
    [Pylance extension](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
  - Set `Pylance` as your Python Language Server
  - Install `boto3-stubs[marketplace-entitlement]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[marketplace-entitlement]'
  ```

  Both type checking and code completion should now work. No explicit type
  annotations required, write your `boto3` code as usual.

  <a id="pycharm"></a>



  Install `boto3-stubs-lite[marketplace-entitlement]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs-lite[marketplace-entitlement]'`
  ```

  Both type checking and code completion should now work. Explicit type
  annotations **are required**.

  Use `boto3-stubs` package instead for implicit type discovery.

  <a id="emacs"></a>



  - Install `boto3-stubs` with services you use in your environment:

  ```bash
  python -m pip install 'boto3-stubs[marketplace-entitlement]'
  ```

  - Install [use-package](https://github.com/jwiegley/use-package),
    [lsp](https://github.com/emacs-lsp/lsp-mode/),
    [company](https://github.com/company-mode/company-mode) and
    [flycheck](https://github.com/flycheck/flycheck) packages
  - Install [lsp-pyright](https://github.com/emacs-lsp/lsp-pyright) package

  ```elisp
  (use-package lsp-pyright
    :ensure t
    :hook (python-mode . (lambda ()
                            (require 'lsp-pyright)
                            (lsp)))  ; or lsp-deferred
    :init (when (executable-find "python3")
            (setq lsp-pyright-python-executable-cmd "python3"))
    )
  ```

  - Make sure emacs uses the environment where you have installed `boto3-stubs`

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="sublime-text"></a>



  - Install `boto3-stubs[marketplace-entitlement]` with services you use in your
    environment:

  ```bash
  python -m pip install 'boto3-stubs[marketplace-entitlement]'
  ```

  - Install [LSP-pyright](https://github.com/sublimelsp/LSP-pyright) package

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="other-ides"></a>



  Not tested, but as long as your IDE supports `mypy` or `pyright`, everything
  should work.

  <a id="mypy"></a>



  - Install `mypy`: `python -m pip install mypy`
  - Install `boto3-stubs[marketplace-entitlement]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[marketplace-entitlement]'`
  ```

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="pyright"></a>



  - Install `pyright`: `npm i -g pyright`
  - Install `boto3-stubs[marketplace-entitlement]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[marketplace-entitlement]'
  ```

  Optionally, you can install `boto3-stubs` to `typings` folder.

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="explicit-type-annotations"></a>



  <a id="client-annotations"></a>



  `MarketplaceEntitlementServiceClient` provides annotations for
  `boto3.client("marketplace-entitlement")`.

  ```python
  from boto3.session import Session

  from mypy_boto3_marketplace_entitlement import MarketplaceEntitlementServiceClient

  client: MarketplaceEntitlementServiceClient = Session().client("marketplace-entitlement")


  ```

  <a id="paginators-annotations"></a>



  `mypy_boto3_marketplace_entitlement.paginator` module contains type annotations
  for all paginators.

  ```python
  from boto3.session import Session

  from mypy_boto3_marketplace_entitlement import MarketplaceEntitlementServiceClient
  from mypy_boto3_marketplace_entitlement.paginator import GetEntitlementsPaginator

  client: MarketplaceEntitlementServiceClient = Session().client("marketplace-entitlement")



  get_entitlements_paginator: GetEntitlementsPaginator = client.get_paginator("get_entitlements")
  ```

  <a id="literals"></a>



  `mypy_boto3_marketplace_entitlement.literals` module contains literals
  extracted from shapes that can be used in user code for type checking.

  ```python
  from mypy_boto3_marketplace_entitlement.literals import (
      GetEntitlementFilterNameType,
      GetEntitlementsPaginatorName,
      MarketplaceEntitlementServiceServiceName,
      ServiceName,
      ResourceServiceName,
      PaginatorName,
      RegionName,
  )


  def check_value(value: GetEntitlementFilterNameType) -> bool:
      ...
  ```

  <a id="typed-dictionaries"></a>



  `mypy_boto3_marketplace_entitlement.type_defs` module contains structures and
  shapes assembled to typed dictionaries for additional type checking.

  ```python
  from mypy_boto3_marketplace_entitlement.type_defs import (
      EntitlementValueTypeDef,
      PaginatorConfigTypeDef,
      GetEntitlementsRequestRequestTypeDef,
      ResponseMetadataTypeDef,
      EntitlementTypeDef,
      GetEntitlementsRequestGetEntitlementsPaginateTypeDef,
      GetEntitlementsResultTypeDef,
  )


  def get_structure() -> EntitlementValueTypeDef:
      return {...}
  ```

  <a id="how-it-works"></a>



  Fully automated
  [mypy-boto3-builder](https://github.com/youtype/mypy_boto3_builder) carefully
  generates type annotations for each service, patiently waiting for `boto3`
  updates. It delivers a drop-in type annotations for you and makes sure that:

  - All available `boto3` services are covered.
  - Each public class and method of every `boto3` service gets valid type
    annotations extracted from the documentation (blame `botocore` docs if types
    are incorrect).
  - Type annotations include up-to-date documentation.
  - Link to documentation is provided for every method.
  - Code is processed by [black](https://github.com/psf/black) and
    [isort](https://github.com/PyCQA/isort) for readability.

  <a id="what's-new"></a>



  <a id="implemented-features"></a>



  - Fully type annotated `boto3`, `botocore` and `aiobotocore` libraries
  - `mypy`, `pyright`, `VSCode`, `PyCharm`, `Sublime Text` and `Emacs`
    compatibility
  - `Client`, `ServiceResource`, `Resource`, `Waiter` `Paginator` type
    annotations for each service
  - Generated `TypeDefs` for each service
  - Generated `Literals` for each service
  - Auto discovery of types for `boto3.client` and `boto3.session` calls
  - Auto discovery of types for `session.client` and `session.session` calls
  - Auto discovery of types for `client.get_waiter` and `client.get_paginator`
    calls
  - Auto discovery of types for `ServiceResource` and `Resource` collections
  - Auto discovery of types for `aiobotocore.Session.create_client` calls

  <a id="latest-changes"></a>



  Builder changelog can be found in
  [Releases](https://github.com/youtype/mypy_boto3_builder/releases).

  <a id="versioning"></a>



  `mypy-boto3-marketplace-entitlement` version is the same as related `boto3`
  version and follows [PEP 440](https://www.python.org/dev/peps/pep-0440/)
  format.

  <a id="thank-you"></a>



  - [Allie Fitter](https://github.com/alliefitter) for
    [boto3-type-annotations](https://pypi.org/project/boto3-type-annotations/),
    this package is based on top of his work
  - [black](https://github.com/psf/black) developers for an awesome formatting
    tool
  - [Timothy Edmund Crosley](https://github.com/timothycrosley) for
    [isort](https://github.com/PyCQA/isort) and how flexible it is
  - [mypy](https://github.com/python/mypy) developers for doing all dirty work
    for us
  - [pyright](https://github.com/microsoft/pyright) team for the new era of typed
    Python

  <a id="documentation"></a>



  All services type annotations can be found in
  [boto3 docs](https://youtype.github.io/boto3_stubs_docs/mypy_boto3_marketplace_entitlement/)

  <a id="support-and-contributing"></a>



  This package is auto-generated. Please reports any bugs or request new features
  in [mypy-boto3-builder](https://github.com/youtype/mypy_boto3_builder/issues/)
  repository.

location_href:
  00014213-python-mypy-boto3-marketplace-entitlement/python3-mypy-boto3-marketplace-entitlement-1.26.0.post1-1.noarch.rpm


pkgId: 193e09c992b5e58b9fb31a73024315ebb0e73c5f6a0a50a9b2569e126b70a342
name: python3-mypy-boto3-resourcegroupstaggingapi
arch: noarch
version: 1.26.0.post1
epoch: 0
release: 1
url: https://github.com/youtype/mypy_boto3_builder
rpm_license: MIT License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-5b584c51-fe24-4390-ada1-44734ed67676
rpm_sourcerpm: python-mypy-boto3-resourcegroupstaggingapi-1.26.0.post1-1.src.rpm
checksum_type: sha256
summary:
  Type annotations for boto3.ResourceGroupsTaggingAPI 1.26.0 service generated with mypy-boto3-builder 7.11.10

description:
  <a id="mypy-boto3-resourcegroupstaggingapi"></a>



  [![PyPI - mypy-boto3-resourcegroupstaggingapi](https://img.shields.io/pypi/v/mypy-boto3-resourcegroupstaggingapi.svg?color=blue)](https://pypi.org/project/mypy-boto3-resourcegroupstaggingapi)
  [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mypy-boto3-resourcegroupstaggingapi.svg?color=blue)](https://pypi.org/project/mypy-boto3-resourcegroupstaggingapi)
  [![Docs](https://img.shields.io/readthedocs/mypy-boto3-builder.svg?color=blue)](https://mypy-boto3-builder.readthedocs.io/)
  [![PyPI - Downloads](https://img.shields.io/pypi/dm/mypy-boto3-resourcegroupstaggingapi?color=blue)](https://pypistats.org/packages/mypy-boto3-resourcegroupstaggingapi)

  ![boto3.typed](https://github.com/youtype/mypy_boto3_builder/raw/main/logo.png)

  Type annotations for
  [boto3.ResourceGroupsTaggingAPI 1.26.0](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/resourcegroupstaggingapi.html#ResourceGroupsTaggingAPI)
  service compatible with [VSCode](https://code.visualstudio.com/),
  [PyCharm](https://www.jetbrains.com/pycharm/),
  [Emacs](https://www.gnu.org/software/emacs/),
  [Sublime Text](https://www.sublimetext.com/),
  [mypy](https://github.com/python/mypy),
  [pyright](https://github.com/microsoft/pyright) and other tools.

  Generated by
  [mypy-boto3-builder 7.11.10](https://github.com/youtype/mypy_boto3_builder).

  More information can be found on
  [boto3-stubs](https://pypi.org/project/boto3-stubs/) page and in
  [mypy-boto3-resourcegroupstaggingapi docs](https://youtype.github.io/boto3_stubs_docs/mypy_boto3_resourcegroupstaggingapi/).

  See how it helps to find and fix potential bugs:

  ![boto3-stubs demo](https://github.com/youtype/mypy_boto3_builder/raw/main/demo.gif)

  - [mypy-boto3-resourcegroupstaggingapi](#mypy-boto3-resourcegroupstaggingapi)
    - [How to install](#how-to-install)
      - [VSCode extension](#vscode-extension)
      - [From PyPI with pip](#from-pypi-with-pip)
    - [How to uninstall](#how-to-uninstall)
    - [Usage](#usage)
      - [VSCode](#vscode)
      - [PyCharm](#pycharm)
      - [Emacs](#emacs)
      - [Sublime Text](#sublime-text)
      - [Other IDEs](#other-ides)
      - [mypy](#mypy)
      - [pyright](#pyright)
    - [Explicit type annotations](#explicit-type-annotations)
      - [Client annotations](#client-annotations)
      - [Paginators annotations](#paginators-annotations)
      - [Literals](#literals)
      - [Typed dictionaries](#typed-dictionaries)
    - [How it works](#how-it-works)
    - [What's new](#what's-new)
      - [Implemented features](#implemented-features)
      - [Latest changes](#latest-changes)
    - [Versioning](#versioning)
    - [Thank you](#thank-you)
    - [Documentation](#documentation)
    - [Support and contributing](#support-and-contributing)

  <a id="how-to-install"></a>



  <a id="vscode-extension"></a>



  Add
  [AWS Boto3](https://marketplace.visualstudio.com/items?itemName=Boto3typed.boto3-ide)
  extension to your VSCode and run `AWS boto3: Quick Start` command.

  Click `Modify` and select `boto3 common` and `ResourceGroupsTaggingAPI`.

  <a id="from-pypi-with-pip"></a>



  Install `boto3-stubs` for `ResourceGroupsTaggingAPI` service.

  ```bash

  python -m pip install 'boto3-stubs[resourcegroupstaggingapi]'




  python -m pip install 'boto3-stubs-lite[resourcegroupstaggingapi]'



  python -m pip install mypy-boto3-resourcegroupstaggingapi
  ```

  <a id="how-to-uninstall"></a>



  ```bash
  python -m pip uninstall -y mypy-boto3-resourcegroupstaggingapi
  ```

  <a id="usage"></a>



  <a id="vscode"></a>



  - Install
    [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
  - Install
    [Pylance extension](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
  - Set `Pylance` as your Python Language Server
  - Install `boto3-stubs[resourcegroupstaggingapi]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[resourcegroupstaggingapi]'
  ```

  Both type checking and code completion should now work. No explicit type
  annotations required, write your `boto3` code as usual.

  <a id="pycharm"></a>



  Install `boto3-stubs-lite[resourcegroupstaggingapi]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs-lite[resourcegroupstaggingapi]'`
  ```

  Both type checking and code completion should now work. Explicit type
  annotations **are required**.

  Use `boto3-stubs` package instead for implicit type discovery.

  <a id="emacs"></a>



  - Install `boto3-stubs` with services you use in your environment:

  ```bash
  python -m pip install 'boto3-stubs[resourcegroupstaggingapi]'
  ```

  - Install [use-package](https://github.com/jwiegley/use-package),
    [lsp](https://github.com/emacs-lsp/lsp-mode/),
    [company](https://github.com/company-mode/company-mode) and
    [flycheck](https://github.com/flycheck/flycheck) packages
  - Install [lsp-pyright](https://github.com/emacs-lsp/lsp-pyright) package

  ```elisp
  (use-package lsp-pyright
    :ensure t
    :hook (python-mode . (lambda ()
                            (require 'lsp-pyright)
                            (lsp)))  ; or lsp-deferred
    :init (when (executable-find "python3")
            (setq lsp-pyright-python-executable-cmd "python3"))
    )
  ```

  - Make sure emacs uses the environment where you have installed `boto3-stubs`

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="sublime-text"></a>



  - Install `boto3-stubs[resourcegroupstaggingapi]` with services you use in your
    environment:

  ```bash
  python -m pip install 'boto3-stubs[resourcegroupstaggingapi]'
  ```

  - Install [LSP-pyright](https://github.com/sublimelsp/LSP-pyright) package

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="other-ides"></a>



  Not tested, but as long as your IDE supports `mypy` or `pyright`, everything
  should work.

  <a id="mypy"></a>



  - Install `mypy`: `python -m pip install mypy`
  - Install `boto3-stubs[resourcegroupstaggingapi]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[resourcegroupstaggingapi]'`
  ```

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="pyright"></a>



  - Install `pyright`: `npm i -g pyright`
  - Install `boto3-stubs[resourcegroupstaggingapi]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[resourcegroupstaggingapi]'
  ```

  Optionally, you can install `boto3-stubs` to `typings` folder.

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="explicit-type-annotations"></a>



  <a id="client-annotations"></a>



  `ResourceGroupsTaggingAPIClient` provides annotations for
  `boto3.client("resourcegroupstaggingapi")`.

  ```python
  from boto3.session import Session

  from mypy_boto3_resourcegroupstaggingapi import ResourceGroupsTaggingAPIClient

  client: ResourceGroupsTaggingAPIClient = Session().client("resourcegroupstaggingapi")


  ```

  <a id="paginators-annotations"></a>



  `mypy_boto3_resourcegroupstaggingapi.paginator` module contains type
  annotations for all paginators.

  ```python
  from boto3.session import Session

  from mypy_boto3_resourcegroupstaggingapi import ResourceGroupsTaggingAPIClient
  from mypy_boto3_resourcegroupstaggingapi.paginator import (
      GetComplianceSummaryPaginator,
      GetResourcesPaginator,
      GetTagKeysPaginator,
      GetTagValuesPaginator,
  )

  client: ResourceGroupsTaggingAPIClient = Session().client("resourcegroupstaggingapi")



  get_compliance_summary_paginator: GetComplianceSummaryPaginator = client.get_paginator(
      "get_compliance_summary"
  )
  get_resources_paginator: GetResourcesPaginator = client.get_paginator("get_resources")
  get_tag_keys_paginator: GetTagKeysPaginator = client.get_paginator("get_tag_keys")
  get_tag_values_paginator: GetTagValuesPaginator = client.get_paginator("get_tag_values")
  ```

  <a id="literals"></a>



  `mypy_boto3_resourcegroupstaggingapi.literals` module contains literals
  extracted from shapes that can be used in user code for type checking.

  ```python
  from mypy_boto3_resourcegroupstaggingapi.literals import (
      ErrorCodeType,
      GetComplianceSummaryPaginatorName,
      GetResourcesPaginatorName,
      GetTagKeysPaginatorName,
      GetTagValuesPaginatorName,
      GroupByAttributeType,
      TargetIdTypeType,
      ResourceGroupsTaggingAPIServiceName,
      ServiceName,
      ResourceServiceName,
      PaginatorName,
      RegionName,
  )


  def check_value(value: ErrorCodeType) -> bool:
      ...
  ```

  <a id="typed-dictionaries"></a>



  `mypy_boto3_resourcegroupstaggingapi.type_defs` module contains structures and
  shapes assembled to typed dictionaries for additional type checking.

  ```python
  from mypy_boto3_resourcegroupstaggingapi.type_defs import (
      ComplianceDetailsTypeDef,
      ResponseMetadataTypeDef,
      FailureInfoTypeDef,
      PaginatorConfigTypeDef,
      GetComplianceSummaryInputRequestTypeDef,
      SummaryTypeDef,
      TagFilterTypeDef,
      GetTagKeysInputRequestTypeDef,
      GetTagValuesInputRequestTypeDef,
      TagTypeDef,
      StartReportCreationInputRequestTypeDef,
      TagResourcesInputRequestTypeDef,
      UntagResourcesInputRequestTypeDef,
      DescribeReportCreationOutputTypeDef,
      GetTagKeysOutputTypeDef,
      GetTagValuesOutputTypeDef,
      TagResourcesOutputTypeDef,
      UntagResourcesOutputTypeDef,
      GetComplianceSummaryInputGetComplianceSummaryPaginateTypeDef,
      GetTagKeysInputGetTagKeysPaginateTypeDef,
      GetTagValuesInputGetTagValuesPaginateTypeDef,
      GetComplianceSummaryOutputTypeDef,
      GetResourcesInputGetResourcesPaginateTypeDef,
      GetResourcesInputRequestTypeDef,
      ResourceTagMappingTypeDef,
      GetResourcesOutputTypeDef,
  )


  def get_structure() -> ComplianceDetailsTypeDef:
      return {...}
  ```

  <a id="how-it-works"></a>



  Fully automated
  [mypy-boto3-builder](https://github.com/youtype/mypy_boto3_builder) carefully
  generates type annotations for each service, patiently waiting for `boto3`
  updates. It delivers a drop-in type annotations for you and makes sure that:

  - All available `boto3` services are covered.
  - Each public class and method of every `boto3` service gets valid type
    annotations extracted from the documentation (blame `botocore` docs if types
    are incorrect).
  - Type annotations include up-to-date documentation.
  - Link to documentation is provided for every method.
  - Code is processed by [black](https://github.com/psf/black) and
    [isort](https://github.com/PyCQA/isort) for readability.

  <a id="what's-new"></a>



  <a id="implemented-features"></a>



  - Fully type annotated `boto3`, `botocore` and `aiobotocore` libraries
  - `mypy`, `pyright`, `VSCode`, `PyCharm`, `Sublime Text` and `Emacs`
    compatibility
  - `Client`, `ServiceResource`, `Resource`, `Waiter` `Paginator` type
    annotations for each service
  - Generated `TypeDefs` for each service
  - Generated `Literals` for each service
  - Auto discovery of types for `boto3.client` and `boto3.session` calls
  - Auto discovery of types for `session.client` and `session.session` calls
  - Auto discovery of types for `client.get_waiter` and `client.get_paginator`
    calls
  - Auto discovery of types for `ServiceResource` and `Resource` collections
  - Auto discovery of types for `aiobotocore.Session.create_client` calls

  <a id="latest-changes"></a>



  Builder changelog can be found in
  [Releases](https://github.com/youtype/mypy_boto3_builder/releases).

  <a id="versioning"></a>



  `mypy-boto3-resourcegroupstaggingapi` version is the same as related `boto3`
  version and follows [PEP 440](https://www.python.org/dev/peps/pep-0440/)
  format.

  <a id="thank-you"></a>



  - [Allie Fitter](https://github.com/alliefitter) for
    [boto3-type-annotations](https://pypi.org/project/boto3-type-annotations/),
    this package is based on top of his work
  - [black](https://github.com/psf/black) developers for an awesome formatting
    tool
  - [Timothy Edmund Crosley](https://github.com/timothycrosley) for
    [isort](https://github.com/PyCQA/isort) and how flexible it is
  - [mypy](https://github.com/python/mypy) developers for doing all dirty work
    for us
  - [pyright](https://github.com/microsoft/pyright) team for the new era of typed
    Python

  <a id="documentation"></a>



  All services type annotations can be found in
  [boto3 docs](https://youtype.github.io/boto3_stubs_docs/mypy_boto3_resourcegroupstaggingapi/)

  <a id="support-and-contributing"></a>



  This package is auto-generated. Please reports any bugs or request new features
  in [mypy-boto3-builder](https://github.com/youtype/mypy_boto3_builder/issues/)
  repository.

location_href:
  00014158-python-mypy-boto3-resourcegroupstaggingapi/python3-mypy-boto3-resourcegroupstaggingapi-1.26.0.post1-1.noarch.rpm


pkgId: 17eb841e108d934d5768715b83bcfea8541dffa791e7e3a9c1e4e58e13f0ea4a
name: python3-mypy-boto3-service-quotas
arch: noarch
version: 1.26.0.post1
epoch: 0
release: 1
summary: Type annotations for boto3.ServiceQuotas 1.26.0 service generated with mypy-boto3-builder 7.11.10
url: https://github.com/youtype/mypy_boto3_builder
rpm_license: MIT License
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-6d73db6f-c7a6-4f77-a26d-75c15768e2e3
rpm_sourcerpm: python-mypy-boto3-service-quotas-1.26.0.post1-1.src.rpm
checksum_type: sha256
description:
  <a id="mypy-boto3-service-quotas"></a>



  [![PyPI - mypy-boto3-service-quotas](https://img.shields.io/pypi/v/mypy-boto3-service-quotas.svg?color=blue)](https://pypi.org/project/mypy-boto3-service-quotas)
  [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mypy-boto3-service-quotas.svg?color=blue)](https://pypi.org/project/mypy-boto3-service-quotas)
  [![Docs](https://img.shields.io/readthedocs/mypy-boto3-builder.svg?color=blue)](https://mypy-boto3-builder.readthedocs.io/)
  [![PyPI - Downloads](https://img.shields.io/pypi/dm/mypy-boto3-service-quotas?color=blue)](https://pypistats.org/packages/mypy-boto3-service-quotas)

  ![boto3.typed](https://github.com/youtype/mypy_boto3_builder/raw/main/logo.png)

  Type annotations for
  [boto3.ServiceQuotas 1.26.0](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/service-quotas.html#ServiceQuotas)
  service compatible with [VSCode](https://code.visualstudio.com/),
  [PyCharm](https://www.jetbrains.com/pycharm/),
  [Emacs](https://www.gnu.org/software/emacs/),
  [Sublime Text](https://www.sublimetext.com/),
  [mypy](https://github.com/python/mypy),
  [pyright](https://github.com/microsoft/pyright) and other tools.

  Generated by
  [mypy-boto3-builder 7.11.10](https://github.com/youtype/mypy_boto3_builder).

  More information can be found on
  [boto3-stubs](https://pypi.org/project/boto3-stubs/) page and in
  [mypy-boto3-service-quotas docs](https://youtype.github.io/boto3_stubs_docs/mypy_boto3_service_quotas/).

  See how it helps to find and fix potential bugs:

  ![boto3-stubs demo](https://github.com/youtype/mypy_boto3_builder/raw/main/demo.gif)

  - [mypy-boto3-service-quotas](#mypy-boto3-service-quotas)
    - [How to install](#how-to-install)
      - [VSCode extension](#vscode-extension)
      - [From PyPI with pip](#from-pypi-with-pip)
    - [How to uninstall](#how-to-uninstall)
    - [Usage](#usage)
      - [VSCode](#vscode)
      - [PyCharm](#pycharm)
      - [Emacs](#emacs)
      - [Sublime Text](#sublime-text)
      - [Other IDEs](#other-ides)
      - [mypy](#mypy)
      - [pyright](#pyright)
    - [Explicit type annotations](#explicit-type-annotations)
      - [Client annotations](#client-annotations)
      - [Paginators annotations](#paginators-annotations)
      - [Literals](#literals)
      - [Typed dictionaries](#typed-dictionaries)
    - [How it works](#how-it-works)
    - [What's new](#what's-new)
      - [Implemented features](#implemented-features)
      - [Latest changes](#latest-changes)
    - [Versioning](#versioning)
    - [Thank you](#thank-you)
    - [Documentation](#documentation)
    - [Support and contributing](#support-and-contributing)

  <a id="how-to-install"></a>



  <a id="vscode-extension"></a>



  Add
  [AWS Boto3](https://marketplace.visualstudio.com/items?itemName=Boto3typed.boto3-ide)
  extension to your VSCode and run `AWS boto3: Quick Start` command.

  Click `Modify` and select `boto3 common` and `ServiceQuotas`.

  <a id="from-pypi-with-pip"></a>



  Install `boto3-stubs` for `ServiceQuotas` service.

  ```bash

  python -m pip install 'boto3-stubs[service-quotas]'




  python -m pip install 'boto3-stubs-lite[service-quotas]'



  python -m pip install mypy-boto3-service-quotas
  ```

  <a id="how-to-uninstall"></a>



  ```bash
  python -m pip uninstall -y mypy-boto3-service-quotas
  ```

  <a id="usage"></a>



  <a id="vscode"></a>



  - Install
    [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
  - Install
    [Pylance extension](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
  - Set `Pylance` as your Python Language Server
  - Install `boto3-stubs[service-quotas]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[service-quotas]'
  ```

  Both type checking and code completion should now work. No explicit type
  annotations required, write your `boto3` code as usual.

  <a id="pycharm"></a>



  Install `boto3-stubs-lite[service-quotas]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs-lite[service-quotas]'`
  ```

  Both type checking and code completion should now work. Explicit type
  annotations **are required**.

  Use `boto3-stubs` package instead for implicit type discovery.

  <a id="emacs"></a>



  - Install `boto3-stubs` with services you use in your environment:

  ```bash
  python -m pip install 'boto3-stubs[service-quotas]'
  ```

  - Install [use-package](https://github.com/jwiegley/use-package),
    [lsp](https://github.com/emacs-lsp/lsp-mode/),
    [company](https://github.com/company-mode/company-mode) and
    [flycheck](https://github.com/flycheck/flycheck) packages
  - Install [lsp-pyright](https://github.com/emacs-lsp/lsp-pyright) package

  ```elisp
  (use-package lsp-pyright
    :ensure t
    :hook (python-mode . (lambda ()
                            (require 'lsp-pyright)
                            (lsp)))  ; or lsp-deferred
    :init (when (executable-find "python3")
            (setq lsp-pyright-python-executable-cmd "python3"))
    )
  ```

  - Make sure emacs uses the environment where you have installed `boto3-stubs`

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="sublime-text"></a>



  - Install `boto3-stubs[service-quotas]` with services you use in your
    environment:

  ```bash
  python -m pip install 'boto3-stubs[service-quotas]'
  ```

  - Install [LSP-pyright](https://github.com/sublimelsp/LSP-pyright) package

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="other-ides"></a>



  Not tested, but as long as your IDE supports `mypy` or `pyright`, everything
  should work.

  <a id="mypy"></a>



  - Install `mypy`: `python -m pip install mypy`
  - Install `boto3-stubs[service-quotas]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[service-quotas]'`
  ```

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="pyright"></a>



  - Install `pyright`: `npm i -g pyright`
  - Install `boto3-stubs[service-quotas]` in your environment:

  ```bash
  python -m pip install 'boto3-stubs[service-quotas]'
  ```

  Optionally, you can install `boto3-stubs` to `typings` folder.

  Type checking should now work. No explicit type annotations required, write
  your `boto3` code as usual.

  <a id="explicit-type-annotations"></a>



  <a id="client-annotations"></a>



  `ServiceQuotasClient` provides annotations for
  `boto3.client("service-quotas")`.

  ```python
  from boto3.session import Session

  from mypy_boto3_service_quotas import ServiceQuotasClient

  client: ServiceQuotasClient = Session().client("service-quotas")


  ```

  <a id="paginators-annotations"></a>



  `mypy_boto3_service_quotas.paginator` module contains type annotations for all
  paginators.

  ```python
  from boto3.session import Session

  from mypy_boto3_service_quotas import ServiceQuotasClient
  from mypy_boto3_service_quotas.paginator import (
      ListAWSDefaultServiceQuotasPaginator,
      ListRequestedServiceQuotaChangeHistoryPaginator,
      ListRequestedServiceQuotaChangeHistoryByQuotaPaginator,
      ListServiceQuotaIncreaseRequestsInTemplatePaginator,
      ListServiceQuotasPaginator,
      ListServicesPaginator,
  )

  client: ServiceQuotasClient = Session().client("service-quotas")



  list_aws_default_service_quotas_paginator: ListAWSDefaultServiceQuotasPaginator = (
      client.get_paginator("list_aws_default_service_quotas")
  )
  list_requested_service_quota_change_history_paginator: ListRequestedServiceQuotaChangeHistoryPaginator = client.get_paginator(
      "list_requested_service_quota_change_history"
  )
  list_requested_service_quota_change_history_by_quota_paginator: ListRequestedServiceQuotaChangeHistoryByQuotaPaginator = client.get_paginator(
      "list_requested_service_quota_change_history_by_quota"
  )
  list_service_quota_increase_requests_in_template_paginator: ListServiceQuotaIncreaseRequestsInTemplatePaginator = client.get_paginator(
      "list_service_quota_increase_requests_in_template"
  )
  list_service_quotas_paginator: ListServiceQuotasPaginator = client.get_paginator(
      "list_service_quotas"
  )
  list_services_paginator: ListServicesPaginator = client.get_paginator("list_services")
  ```

  <a id="literals"></a>



  `mypy_boto3_service_quotas.literals` module contains literals extracted from
  shapes that can be used in user code for type checking.

  ```python
  from mypy_boto3_service_quotas.literals import (
      ErrorCodeType,
      ListAWSDefaultServiceQuotasPaginatorName,
      ListRequestedServiceQuotaChangeHistoryByQuotaPaginatorName,
      ListRequestedServiceQuotaChangeHistoryPaginatorName,
      ListServiceQuotaIncreaseRequestsInTemplatePaginatorName,
      ListServiceQuotasPaginatorName,
      ListServicesPaginatorName,
      PeriodUnitType,
      RequestStatusType,
      ServiceQuotaTemplateAssociationStatusType,
      ServiceQuotasServiceName,
      ServiceName,
      ResourceServiceName,
      PaginatorName,
      RegionName,
  )


  def check_value(value: ErrorCodeType) -> bool:
      ...
  ```

  <a id="typed-dictionaries"></a>



  `mypy_boto3_service_quotas.type_defs` module contains structures and shapes
  assembled to typed dictionaries for additional type checking.

  ```python
  from mypy_boto3_service_quotas.type_defs import (
      DeleteServiceQuotaIncreaseRequestFromTemplateRequestRequestTypeDef,
      ErrorReasonTypeDef,
      GetAWSDefaultServiceQuotaRequestRequestTypeDef,
      ResponseMetadataTypeDef,
      GetRequestedServiceQuotaChangeRequestRequestTypeDef,
      RequestedServiceQuotaChangeTypeDef,
      GetServiceQuotaIncreaseRequestFromTemplateRequestRequestTypeDef,
      ServiceQuotaIncreaseRequestInTemplateTypeDef,
      GetServiceQuotaRequestRequestTypeDef,
      PaginatorConfigTypeDef,
      ListAWSDefaultServiceQuotasRequestRequestTypeDef,
      ListRequestedServiceQuotaChangeHistoryByQuotaRequestRequestTypeDef,
      ListRequestedServiceQuotaChangeHistoryRequestRequestTypeDef,
      ListServiceQuotaIncreaseRequestsInTemplateRequestRequestTypeDef,
      ListServiceQuotasRequestRequestTypeDef,
      ListServicesRequestRequestTypeDef,
      ServiceInfoTypeDef,
      ListTagsForResourceRequestRequestTypeDef,
      TagTypeDef,
      MetricInfoTypeDef,
      PutServiceQuotaIncreaseRequestIntoTemplateRequestRequestTypeDef,
      QuotaPeriodTypeDef,
      RequestServiceQuotaIncreaseRequestRequestTypeDef,
      UntagResourceRequestRequestTypeDef,
      GetAssociationForServiceQuotaTemplateResponseTypeDef,
      GetRequestedServiceQuotaChangeResponseTypeDef,
      ListRequestedServiceQuotaChangeHistoryByQuotaResponseTypeDef,
      ListRequestedServiceQuotaChangeHistoryResponseTypeDef,
      RequestServiceQuotaIncreaseResponseTypeDef,
      GetServiceQuotaIncreaseRequestFromTemplateResponseTypeDef,
      ListServiceQuotaIncreaseRequestsInTemplateResponseTypeDef,
      PutServiceQuotaIncreaseRequestIntoTemplateResponseTypeDef,
      ListAWSDefaultServiceQuotasRequestListAWSDefaultServiceQuotasPaginateTypeDef,
      ListRequestedServiceQuotaChangeHistoryByQuotaRequestListRequestedServiceQuotaChangeHistoryByQuotaPaginateTypeDef,
      ListRequestedServiceQuotaChangeHistoryRequestListRequestedServiceQuotaChangeHistoryPaginateTypeDef,
      ListServiceQuotaIncreaseRequestsInTemplateRequestListServiceQuotaIncreaseRequestsInTemplatePaginateTypeDef,
      ListServiceQuotasRequestListServiceQuotasPaginateTypeDef,
      ListServicesRequestListServicesPaginateTypeDef,
      ListServicesResponseTypeDef,
      ListTagsForResourceResponseTypeDef,
      TagResourceRequestRequestTypeDef,
      ServiceQuotaTypeDef,
      GetAWSDefaultServiceQuotaResponseTypeDef,
      GetServiceQuotaResponseTypeDef,
      ListAWSDefaultServiceQuotasResponseTypeDef,
      ListServiceQuotasResponseTypeDef,
  )


  def get_structure() -> DeleteServiceQuotaIncreaseRequestFromTemplateRequestRequestTypeDef:
      return {...}
  ```

  <a id="how-it-works"></a>



  Fully automated
  [mypy-boto3-builder](https://github.com/youtype/mypy_boto3_builder) carefully
  generates type annotations for each service, patiently waiting for `boto3`
  updates. It delivers a drop-in type annotations for you and makes sure that:

  - All available `boto3` services are covered.
  - Each public class and method of every `boto3` service gets valid type
    annotations extracted from the documentation (blame `botocore` docs if types
    are incorrect).
  - Type annotations include up-to-date documentation.
  - Link to documentation is provided for every method.
  - Code is processed by [black](https://github.com/psf/black) and
    [isort](https://github.com/PyCQA/isort) for readability.

  <a id="what's-new"></a>



  <a id="implemented-features"></a>



  - Fully type annotated `boto3`, `botocore` and `aiobotocore` libraries
  - `mypy`, `pyright`, `VSCode`, `PyCharm`, `Sublime Text` and `Emacs`
    compatibility
  - `Client`, `ServiceResource`, `Resource`, `Waiter` `Paginator` type
    annotations for each service
  - Generated `TypeDefs` for each service
  - Generated `Literals` for each service
  - Auto discovery of types for `boto3.client` and `boto3.session` calls
  - Auto discovery of types for `session.client` and `session.session` calls
  - Auto discovery of types for `client.get_waiter` and `client.get_paginator`
    calls
  - Auto discovery of types for `ServiceResource` and `Resource` collections
  - Auto discovery of types for `aiobotocore.Session.create_client` calls

  <a id="latest-changes"></a>



  Builder changelog can be found in
  [Releases](https://github.com/youtype/mypy_boto3_builder/releases).

  <a id="versioning"></a>



  `mypy-boto3-service-quotas` version is the same as related `boto3` version and
  follows [PEP 440](https://www.python.org/dev/peps/pep-0440/) format.

  <a id="thank-you"></a>



  - [Allie Fitter](https://github.com/alliefitter) for
    [boto3-type-annotations](https://pypi.org/project/boto3-type-annotations/),
    this package is based on top of his work
  - [black](https://github.com/psf/black) developers for an awesome formatting
    tool
  - [Timothy Edmund Crosley](https://github.com/timothycrosley) for
    [isort](https://github.com/PyCQA/isort) and how flexible it is
  - [mypy](https://github.com/python/mypy) developers for doing all dirty work
    for us
  - [pyright](https://github.com/microsoft/pyright) team for the new era of typed
    Python

  <a id="documentation"></a>



  All services type annotations can be found in
  [boto3 docs](https://youtype.github.io/boto3_stubs_docs/mypy_boto3_service_quotas/)

  <a id="support-and-contributing"></a>



  This package is auto-generated. Please reports any bugs or request new features
  in [mypy-boto3-builder](https://github.com/youtype/mypy_boto3_builder/issues/)
  repository.

location_href:
  00014278-python-mypy-boto3-service-quotas/python3-mypy-boto3-service-quotas-1.26.0.post1-1.noarch.rpm


pkgId: 595e11b468239d1b8a156214ab882fe58d4d8b493a40165a4fa3c3b9ce920203
name: python3-proto-google-cloud-datastore-v1
arch: noarch
version: 0.90.4
epoch: 0
release: 1
summary: GRPC library for the Google Cloud Datastore API
url: https://github.com/googleapis/googleapis
rpm_license: Apache-2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-7a0efffd-9f3c-4947-b95b-d96fddf23be2
rpm_sourcerpm: python-proto-google-cloud-datastore-v1-0.90.4-1.src.rpm
checksum_type: sha256
description:
  gRPC library for Google Cloud Datastore API

  proto-google-cloud-datastore-v1 is the IDL-derived library for the datastore (v1) service in the googleapis_ repository.

  .. _`googleapis`: https://github.com/googleapis/googleapis/tree/master/google/datastore/v1

location_href:
  00010270-python-proto-google-cloud-datastore-v1/python3-proto-google-cloud-datastore-v1-0.90.4-1.noarch.rpm


pkgId: eea638efa30634cacd86ec049932d9f7cfd4ed059a2fc7739a1333c82dd8f601
name: python3-proto-google-cloud-error-reporting-v1beta1
arch: noarch
version: 0.15.3
epoch: 0
release: 1
summary: GRPC library for the Stackdriver Error Reporting API
url: https://github.com/googleapis/googleapis
rpm_license: Apache-2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-456f7807-ff0f-48bd-8c6c-a9be3f7675d1
rpm_sourcerpm: python-proto-google-cloud-error-reporting-v1beta1-0.15.3-1.src.rpm
checksum_type: sha256
description:
  gRPC library for Stackdriver Error Reporting API

  proto-google-cloud-error-reporting-v1beta1 is the IDL-derived library for the error-reporting (v1beta1) service in the googleapis_ repository.

  .. _`googleapis`: https://github.com/googleapis/googleapis/tree/master/google/devtools/clouderrorreporting/v1beta1

location_href:
  00011948-python-proto-google-cloud-error-reporting-v1beta1/python3-proto-google-cloud-error-reporting-v1beta1-0.15.3-1.noarch.rpm


pkgId: 4b834f28e295a6367f08ab5b72430cc418ee2dc26bbe47817d55806cb94612c0
name: python3-proto-google-cloud-logging-v2
arch: noarch
version: 0.91.3
epoch: 0
release: 1
summary: GRPC library for the Stackdriver Logging API
url: https://github.com/googleapis/googleapis
rpm_license: Apache-2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
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rpm_buildhost: resalloc-a9c890cc-0c87-427e-9aae-04d9fa419597
rpm_sourcerpm: python-proto-google-cloud-logging-v2-0.91.3-1.src.rpm
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description:
  gRPC library for Stackdriver Logging API

  proto-google-cloud-logging-v2 is the IDL-derived library for the logging (v2) service in the googleapis_ repository.

  .. _`googleapis`: https://github.com/googleapis/googleapis/tree/master/google/logging/v2

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pkgId: bb3a122d1bd559561c2c9c85d4e1a383f02acbf286704014275a125e9779ebd8
name: python3-proto-google-cloud-pubsub-v1
arch: noarch
version: 0.15.4
epoch: 0
release: 1
summary: GRPC library for the Google Cloud Pub/Sub API
url: https://github.com/googleapis/googleapis
rpm_license: Apache-2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-0e2fd6d8-3ebc-459c-bbfc-b0a764b02f55
rpm_sourcerpm: python-proto-google-cloud-pubsub-v1-0.15.4-1.src.rpm
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description:
  gRPC library for Google Cloud Pub/Sub API

  proto-google-cloud-pubsub-v1 is the IDL-derived library for the pubsub (v1) service in the googleapis_ repository.

  .. _`googleapis`: https://github.com/googleapis/googleapis/tree/master/google/pubsub/v1

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pkgId: e6229caa5369ecea31fa834c837f3dbb6f6e9dfda182c1c2d465c63997208804
name: python3-proto-google-cloud-spanner-admin-database-v1
arch: noarch
version: 0.15.3
epoch: 0
release: 1
summary: GRPC library for the Cloud Spanner Database Admin API
url: https://github.com/googleapis/googleapis
rpm_license: Apache-2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-8219472e-4fed-4793-9a0c-0ba83c47bc25
rpm_sourcerpm: python-proto-google-cloud-spanner-admin-database-v1-0.15.3-1.src.rpm
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description:
  gRPC library for Cloud Spanner Database Admin API

  proto-google-cloud-spanner-admin-database-v1 is the IDL-derived library for the spanner-admin-database (v1) service in the googleapis_ repository.

  .. _`googleapis`: https://github.com/googleapis/googleapis/tree/master/google/spanner/admin/database/v1

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pkgId: e802098c68bce7d8c0bf3521f364b96b60206a7d3f865b5190956dd2148056d7
name: python3-proto-google-cloud-spanner-admin-instance-v1
arch: noarch
version: 0.15.3
epoch: 0
release: 1
summary: GRPC library for the Cloud Spanner Instance Admin API
url: https://github.com/googleapis/googleapis
rpm_license: Apache-2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
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rpm_sourcerpm: python-proto-google-cloud-spanner-admin-instance-v1-0.15.3-1.src.rpm
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description:
  gRPC library for Cloud Spanner Instance Admin API

  proto-google-cloud-spanner-admin-instance-v1 is the IDL-derived library for the spanner-admin-instance (v1) service in the googleapis_ repository.

  .. _`googleapis`: https://github.com/googleapis/googleapis/tree/master/google/spanner/admin/instance/v1

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pkgId: aefc8075b5e75542823996e1900fdb4e2e1f40a1193546d3002899d13d3af2d6
name: python3-proto-google-cloud-spanner-v1
arch: noarch
version: 0.15.3
epoch: 0
release: 1
summary: GRPC library for the Cloud Spanner API
url: https://github.com/googleapis/googleapis
rpm_license: Apache-2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-ce39e242-cfbb-4b72-8239-1313e054961f
rpm_sourcerpm: python-proto-google-cloud-spanner-v1-0.15.3-1.src.rpm
checksum_type: sha256
description:
  gRPC library for Cloud Spanner API

  proto-google-cloud-spanner-v1 is the IDL-derived library for the spanner (v1) service in the googleapis_ repository.

  .. _`googleapis`: https://github.com/googleapis/googleapis/tree/master/google/spanner/v1

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pkgId: 70fb1276a46e412ebbd24e05609127ea8d366c87d4d308f7d85a5016ac51f79c
name: python3-pytest-github-actions-annotate-failures
arch: noarch
version: 0.1.8
epoch: 0
release: 1
summary: pytest plugin to annotate failed tests with a workflow command for GitHub Actions
url: https://github.com/pytest-dev/pytest-github-actions-annotate-failures
rpm_license: MIT
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-556205f2-2ec2-42e3-8802-3811482442da
rpm_sourcerpm: python-pytest-github-actions-annotate-failures-0.1.8-1.src.rpm
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description:
  [Pytest](https://pypi.org/project/pytest/) plugin to annotate failed tests with a [workflow command for GitHub Actions](https://help.github.com/en/actions/reference/workflow-commands-for-github-actions)


  Just install and run pytest with this plugin in your workflow. For example,

  ```yaml
  name: test

  on:
    push:

  jobs:
    test:
      runs-on: ubuntu-latest

      steps:
      - uses: actions/checkout@v3

      - uses: actions/setup-python@v4
        with:
          python-version: 3.7

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt

      - name: Install plugin
        run: pip install pytest-github-actions-annotate-failures

      - run: pytest
  ```

  If your test is running in a Docker container, you have to install this plugin and manually set `GITHUB_ACTIONS` environment variable to `true` inside of Docker container. (For example, `docker-compose run --rm -e GITHUB_ACTIONS=true app -- pytest`)

  If your tests are run from a subdirectory of the git repository, you have to set the `PYTEST_RUN_PATH` environment variable to the path of that directory relative to the repository root in order for GitHub to identify the files with errors correctly.


  [![Image from Gyazo](https://i.gyazo.com/b578304465dd1b755ceb0e04692a57d9.png)](https://gyazo.com/b578304465dd1b755ceb0e04692a57d9)

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pkgId: aad31887534f5c91329b19da8cbc726f48221f89f48f7bd74561bf4ffd0c37c4
name: python3-robotframework-appiumlibrary
arch: noarch
version: 2.0.0
epoch: 0
release: 1
summary: Robot Framework Mobile app testing library for Appium Client Android & iOS & Web
url: https://github.com/serhatbolsu/robotframework-appiumlibrary
rpm_license: Apache License 2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-b2278efb-0df1-4219-b7d4-f450fea061aa
rpm_sourcerpm: python-robotframework-appiumlibrary-2.0.0-1.src.rpm
checksum_type: sha256
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pkgId: 06116129c090588c5662b5912e8ddc72beed1484bf098843ff4b299a1c5ff0c0
name: python3-robotframework-selenium2library
arch: noarch
version: 3.0.0
epoch: 0
release: 1
summary: Web testing library for Robot Framework
url: https://github.com/robotframework/Selenium2Library
rpm_license: Apache License 2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-f4b6cf42-cb60-4898-b544-92159d9a3e36
rpm_sourcerpm: python-robotframework-selenium2library-3.0.0-1.src.rpm
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description:
  Selenium2Library is a web testing library for `Robot Framework`_
  that uses the Selenium_ tool internally. The project is hosted on
  GitHub_ and downloads can be found from PyPI_.
  Starting from version 3.0, Selenium2Library is renamed to SeleniumLibrary_
  and this project exists mainly to help with transitioning.

location_href:
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pkgId: 90ac5adbe677ddef87c8fdc082dc46741d322efdedd169f7a3a55ec9b105666a
name: python3-robotframework-seleniumlibrary
arch: noarch
version: 6.0.0
epoch: 0
release: 1
summary: Web testing library for Robot Framework
url: https://github.com/robotframework/SeleniumLibrary
rpm_license: Apache License 2.0
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-7d9579c2-4d29-4e30-a8dd-8f6f5e59519c
rpm_sourcerpm: python-robotframework-seleniumlibrary-6.0.0-1.src.rpm
checksum_type: sha256
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pkgId: 08b47e0af9e6934eb19fa30a034fdc90a7ae4512674a8ae1fd436511ae3d765b
name: python3-sphinxcontrib-serializinghtml
arch: noarch
version: 1.1.5
epoch: 0
release: 1
url: http://sphinx-doc.org/
rpm_license: BSD
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-4217389d-67e9-4955-9f41-2eb40bd4cb24
rpm_sourcerpm: python-sphinxcontrib-serializinghtml-1.1.5-1.src.rpm
checksum_type: sha256
summary:
  sphinxcontrib-serializinghtml is a sphinx extension which outputs "serialized" HTML files (json and pickle).

description:
  sphinxcontrib-serializinghtml is a sphinx extension which outputs
  "serialized" HTML files (json and pickle).

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pkgId: 967f16fc11283175b15e94dac4938bbce36af120f4676686e8bba8808fda7d4a
name: python3-sphinxcontrib-svg2pdfconverter
arch: noarch
version: 1.2.2
epoch: 0
release: 1
summary: Sphinx SVG to PDF converter extension
url: https://github.com/missinglinkelectronics/sphinxcontrib-svg2pdfconverter
rpm_license: BSD
rpm_vendor: openEuler Copr - user mywaaagh_admin
rpm_group: Unspecified
rpm_buildhost: resalloc-2af7c6f8-5b94-48e3-a375-a94476809c32
rpm_sourcerpm: python-sphinxcontrib-svg2pdfconverter-1.2.2-1.src.rpm
checksum_type: sha256
description:
  Just install via ``pip``:
     $ pip install sphinxcontrib-svg2pdfconverter
  You can choose between Inkscape, libRSVG and CairoSVG by adding
  ``sphinxcontrib.inkscapeconverter``, ``sphinxcontrib.rsvgconverter`` or
  ``sphinxcontrib.cairosvgconverter`` to the ``extensions`` list in your
  ``conf.py``.
  Make sure to have either ``inkscape`` or the ``rsvg-convert`` command available
  in your systems ``PATH`` and, if necessary, adapt the
  ``inkscape_converter_bin`` or ``rsvg_converter_bin`` config value respectively.
  CairoSVG requires additional dependencies to be installed with:
     $ pip install sphinxcontrib-svg2pdfconverter[CairoSVG]
  CairoSVG and its dependencies may require additional tools during the
  installation depending on the OS you are using; see the `CairoSVG documentation
  <https://cairosvg.org/documentation/#installation>`_ for further details.

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