semvec/pypi-packages huggingface dataset:
awssamlpy2 1.0.8.2
SAML federated API access for AWS

As part of AWS Governance to enhance the security with the accounts and IAM users, Federated API access is recommended for AWS resources instead of hard-coded IAM AccessID and SecretKeys in the config file.

# Steps:

- If this is the first time install of this python package, use below command:

		For Python2.x version, pip install awssamlpy2
		For Python3.x version, pip install awssamlpy3

- To upgrade this python package to latest version, use below command:

		For Python2.x version, pip install awssamlpy2 --upgrade
		For Python3.x version, pip install awssamlpy3 --upgrade

- Create a '.awssaml.properties' (~/.awssaml.properties) file under your user home directory like below -

		[UserProp]
		aws-region=us-east-1
		aws-outputformat=json
		idpurl=https://<Your Company AWS SAML Domain>/adfs/ls/IdpInitiatedSignOn.aspx?loginToRp=urn:amazon:webservices

	Please refer to Mesh DOC-111675 for the idpurl

- Create a 'configure' file under ~/.aws directory without providing the values for access and secret keys. Dont worry if haven't yet; the package will automatically create one if this file is not present. You may just choose the output and region fields as per your need.

		[default]
		output = json
		region = us-east-1
		aws_access_key_id =
		aws_secret_access_key =

- We have taken care to package the required modules. But if there are any additional packages required, install the missing modules based on the error encountered like below:

        On Linux, pip install <module>
            Eg: pip install requests

        On Windows, easy_install <module>
            Eg: easy_install requests

- Whenever you need SAML access to your AWS services, just the command:

		aws-saml 

- This does the following:

        - Verifies your ~/.aws/configure file to set the approriate region; OR creates one if its not present

		- Prompts the user for AD username/password and does SAML auth with our ADFS
                    NOTE: Username has to be in the format <domain>\<networkID>

		- Based on SAML response, prompts the user to choose the roles available on AWS for that user

		- Then, stores the temporarily created credentials (using Amazon STS service) for the user in the ~/.aws/credentials file along with STS token

		- Use API calls to work on AWS resources

		- Sample API call used in the script is for listing the S3 buckets, which is in Boto2.x format
m2m-token 0.0.2
Generates token for machine-to-machine authentication
Requires Python: >=3.6

# M2M Token

Here is the issue : You have a Bot and a REST Api that needs to communicate. 
As the REST Api is an entrypoint to your system, you don't want it to be unsecured (at least you should not want that.)
At the same time, you don't want them to have plain text password configured somewhere, because, it can be guessed.

And you don't want to implement a full-fledged 2-ways SSL authentication, because, well, it is complicated.

## So what to do then ?
It'd be nice if we had a way to have a token that changes regularly but can still be guessed by involved parties.

This is something that has been (still ?) done on automated garage doors. Ever wondered how come your remote does not
open the garage door of your neighbor ? It's because it uses a [Rolling Code](https://en.wikipedia.org/wiki/Rolling_code)
which is a quite simple technique but still pretty efficient.

## How does it works ?
The same as in the rolling code for the RF remote controller, both parties (Bot and REST Api for instance),
are going to share a `seed`. This seed will be used to generate a token on both sides, this way we can check that
the Bot is indeed authorize to use the REST Api.

The parties are also going to agree on the token `ttl` (Time To Live), this should prevent attacker
from sending an outdated token to the REST Api and be granted access.

## How to use ?

### Installation
`pip install m2m_token`

### Token Generation
```python
from m2m_token.token import generate

seed = 'VeryComplicatedSeedSoThatItIsReallyHardForTheAttackerToGuessIt'
ttl = 3  # Time in seconds the token is going to be valid.

# On the bot side:
bot_token = generate(seed, ttl)
# Add the generated token to the REST Http request

# On the API side:
def method_that_handles_request(request):
    token = find_token_in_request(request)
    if token !=  generate(seed, ttl):
        raise NotAllowed()
```

### `generate()` Parameters
Parameter|Description|Mandatory|Default Value
---|---|---|---|
`seed`|The seed to generate the token|Yes|N/A
`ttl`|Time in seconds the token is valid|Yes|N/A
`sequence`|Characters sequence from wich the token will be generated|No|`string.ascii_letters + string.digits`
`token_len`|Generated token length|No|6

## Disclaimer
This comes without any warranties of any sorts. 
I can not be hold responsible if you ever stumble upon this and decide to do something evil with it (like training some raccoon to hold a bazooka and go berserk with it).

More seriously, this has not been tested by real pentesters (if any are willing I'd be more than glad to have feedbacks)

Pull requests, issues may or may not be handled but are always welcomed.
leslie 0.0.1rc1
an nlp training framework base tf2.0
Requires Python: >=3.6.1

#  Leslie
<div align="center">
    <p>
    An MIT NLP research library, built on TensorFlow, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.
    </p>
    <hr/>
</div>


## Models Support
text classifier：  
1. [average_pooling_text_classifier](https://github.com/whu-SpongeBob/spongenlp/blob/master/spongenlp/models/text_classifier/aver_pooling_text_classifier.py)  
2. [bert_text_classifier](https://github.com/whu-SpongeBob/spongenlp/blob/master/spongenlp/models/text_classifier/bert_text_classifier_model.py)  
3. [cnn_text_classifier](https://github.com/whu-SpongeBob/spongenlp/blob/master/spongenlp/models/text_classifier/cnn_test_classifier.py)  

intent slot multitask:
1. [sf_id](https://github.com/whu-SpongeBob/spongenlp/blob/master/spongenlp/models/intent_slot/sf_id_intent_slot_model.py)

## Paper With Code
1. [A Novel Bi-directional Interrelated Model for Joint Intent Detection and
Slot Filling
](https://github.com/whu-SpongeBob/spongenlp/blob/master/spongenlp/models/intent_slot/sf_id_intent_slot_model.py)