semvec/pypi-packages huggingface dataset:
script2bundle 0.3.0
Generate an application bundle (MacOS) from an executable

## Short description

script2bundle is a command line Python script that bundles an executable, e.g. another (Python) script, into a MacOS application bundle. Compared to [py2app](https://py2app.readthedocs.io/en/latest/), this is a minmal wrapper and allows editable installs (PEP660), but the application bundle will only work on the computer it was created.  Script2bundle is of limited use for command line scripts, where [Platypus](https://sveinbjorn.org/platypus) might be a better option. It is intended to be used for GUI (e.g. Qt) executables.

## How to use
Simply run the script without any options to generate an example file. Afterwards, you will find example.app in the same folder.

## General options:
- -e The filename of the (existing) executable file to be bundled.
- -f The filename of the app to be generated (without .app).
- -i The (existing) png file to be used to generate an icon.
- -d The destination of the .app file:  user (~/Applications), system (/Applications) or executable (same as -e).
- --launch Launch the app to register properly.
- --terminal Launch the app via a Terminal

## Options to connect a file extension
- -x An (app specific!) file extension to be opened by the app.
- --CFBundleTypeRole The app’s role with respect to the file extension. Can be Editor, Viewer, Shell or None.

## Additional modifier options:
The information above will be used to generate reasonable entries in `Info.plist`. However, these entries can be directly modified using the corresponding argument named according to the [Apple documentation](https://developer.apple.com/library/archive/documentation/General/Reference/InfoPlistKeyReference/Articles/CoreFoundationKeys.html). The implemented options are:

- --CFBundleDisplayName Specifies the display name of the bundle, visible to users and used by Siri.

## Notes
Due to the internal structure of some entries, they have to be formatted according to [RFC 1035](https://datatracker.ietf.org/doc/html/rfc1035). If neccessary, an error is raised by script2bundle, e.g. caused by two subsequent dashes in the filename.
envquest 0.0.12
A collection of Reinforcement Learning algorithms to train autonomous agents in different environments.
Requires Python: >=3.8

# EnvQuest
Train and evaluate your autonomous agents in different environments using a collection of RL algorithms.

## Installation
To install the EnvQuest library, use `pip install envquest`.

## Usage

### Run a simple gym environment
```python
from envquest import envs, agents

# Instantiate an environment
env = envs.gym.GymEnvironment.from_task("LunarLander-v3")

# Instantiate an agent
agent = agents.generics.RandomAgent(env.observation_space, env.action_space)

# Execute an MDP
timestep = env.reset()

while not timestep.last():
    observation = timestep.observation
    action = agent.act(observation=observation)
    timestep = env.step(action)

# Render the environment
frame = env.render(256, 256)
```

### Usage with metaworld
```python
import metaworld
import random

from envquest import envs

ml1 = metaworld.ML1("basketball-v2")
task = random.choice(ml1.train_tasks)
env = ml1.train_classes["basketball-v2"](render_mode="rgb_array")
env.set_task(task)

env = envs.gym.GymEnvironment.from_env(env)
```

### Train a DQN Agent in a gym environment

First, set up a WandB logging environment
```shell
# Install wandb
pip install wandb

# Start a wandb local server
wandb server start
```

Then, train a DQN agent in a gym's CartPole-v1 environment.

```python
from envquest import arguments, envs, agents, trainers

# Define training arguments
args = arguments.TrainingArguments(
    env=arguments.EnvArguments(task="CartPole-v1"),
    agent=arguments.DQNAgentArguments(),
    logging=arguments.LoggingArguments(save_agent_snapshots=False)
)

# Instantiate an environment
env = envs.gym.GymEnvironment.from_task(task=args.env.task, max_episode_length=args.env.max_episode_length)

# Instantiate a DQN Agent
agent = agents.dqn_agents.DiscreteQNetAgent(
    mem_capacity=args.agent.mem_capacity,
    discount=args.agent.discount,
    n_steps=args.agent.n_steps,
    lr=args.agent.lr,
    tau=args.agent.tau,
    eps_start=args.agent.eps_start,
    eps_end=args.agent.eps_end,
    eps_step_duration=args.agent.eps_step_duration,
    eps_decay=args.agent.eps_decay,
    observation_space=env.observation_space,
    action_space=env.action_space,
)

# Instantiate a trainer
trainer = trainers.td_trainers.TDTrainer(env, agent, args)

# Start training
trainer.train()
```

Track the performances of your agent on wandb: http://localhost:8080/

## Examples
See some examples in the [examples](https://github.com/medric49/envquest/tree/master/examples) folder.
t5omass 1.3
This is a library to check email (Hotmail, Gmail) if is availabe, get instagram information by usernam, get reset of user instagram, check if email registered in instagram, And many more features that may be added soon.
Requires Python: >=3.6

<h3 align="center">A Turkey developer [thomas] </h3>

<h3 align="left">Connect with me:</h3>
<p align="left">
<a href="https://t.me/t5omas" target="blank"><img align="center" src="https://raw.githubusercontent.com/rahuldkjain/github-profile-readme-generator/master/src/images/icons/Social/devto.svg" alt="thomas" height="30" width="40" /></a>
</p>

<h3 align="left">Languages and Tools:</h3>
<p align="left"> <a href="https://www.w3schools.com/cpp/" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/cplusplus/cplusplus-original.svg" alt="cplusplus" width="40" height="40"/> </a> <a href="https://www.w3schools.com/css/" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/css3/css3-original-wordmark.svg" alt="css3" width="40" height="40"/> </a> <a href="https://www.figma.com/" target="_blank" rel="noreferrer"> <img src="https://www.vectorlogo.zone/logos/figma/figma-icon.svg" alt="figma" width="40" height="40"/> </a> <a href="https://www.w3.org/html/" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/html5/html5-original-wordmark.svg" alt="html5" width="40" height="40"/> </a> <a href="https://www.adobe.com/in/products/illustrator.html" target="_blank" rel="noreferrer"> <img src="https://www.vectorlogo.zone/logos/adobe_illustrator/adobe_illustrator-icon.svg" alt="illustrator" width="40" height="40"/> </a> <a href="https://www.photoshop.com/en" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/photoshop/photoshop-line.svg" alt="photoshop" width="40" height="40"/> </a> <a href="https://www.python.org" target="_blank" rel="noreferrer"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/python/python-original.svg" alt="python" width="40" height="40"/> </a> </p>

## For install The Tool ##

pip install t5omass

## For Update The Tool ##

t5omass --upgrade