semvec/pypi-packages huggingface dataset:
Topython 0.3
Best library to check Instagram applications
Requires Python: >=3.6



```bash
pip install ToPython

```bash
#Check the email it is linked to Instagram or not 

from ToPython import Instagram

check = Instagram.CheckEmail("Your Email +@")

if check == True:
    print("Valid Email")
else:
    print("Invalid Email")
    

# Instagram Login (Email-passwod , Username-password , Phone-password)

from ToPython import Instagram

check = Instagram.Login("Your Email-Username","Your Password")

if check == True :
    print("Done Login")
elif check == False :
    print("Login failed")
else :
    print("ban")        

#Check the Usernames available on Instagram 

from ToPython import Instagram

check = Instagram.CheckUsers("Your Username ")

if check == True :
    print("Available Username")
elif check == False :
    print("Unavailable Username")
else :
    print("ban") 

# Fetch specific Instagram account information 

from ToPython import Instagram

info_ig = Instagram.information("Your Username")

print(info_ig) # Can You Use json

# Send a message to reset your Instagram account password 

from ToPython import Instagram

reset = Instagram.Rests("Email-User") # Email or Username

print(reset) 

# Get Usernames from Instagram 

from ToPython import Instagram

Generate = Instagram.GenUsers()
if Generate == None:
    print(None)
else:
    print(Generate)
tokenmeter 0.1.0
A library for calculating and estimating costs of language model usage
Requires Python: >=3.6
Keywords: nlp, language models, cost estimation, openai

# TokenMeter

TokenMeter is a Python library for calculating and estimating costs associated with using various language models. It provides tools for token counting, cost calculation, and includes integration with OpenAI's API.

## Features

- Calculate costs for language model usage based on input and output tokens
- Estimate costs before making API calls
- Retrieve model information including pricing and token limits
- Integrated OpenAI API handler for easy response generation
- Utility functions for token counting, text truncation, and text splitting

## Installation

You can install TokenMeter using pip:

```bash
pip install tokenmeter
```

## Usage

Here are some basic usage examples:

```python
from tokenmeter import TokenMeter, calculate_cost, estimate_cost, get_model_info, use_openai

# Initialize TokenMeter
tm = TokenMeter()

# Calculate cost for a completed interaction
model_name = "gpt-3.5-turbo"
prompt = "Tell me a joke about programming."
response = "Why do programmers prefer dark mode? Because light attracts bugs!"
cost = tm.calculate_cost(model_name, prompt, response)
print(f"Actual cost: ${cost['total_cost']:.6f}")

# Estimate cost before making an API call
estimated_output_tokens = 50
estimated_cost = tm.estimate_cost(model_name, prompt, estimated_output_tokens)
print(f"Estimated cost: ${estimated_cost['total_cost']:.6f}")

# Get model information
model_info = tm.get_model_info(model_name)
print(f"Model info: {model_info}")


# You can also use the convenience functions directly
direct_cost = calculate_cost(model_name, prompt, response)
print(f"Direct cost calculation: ${direct_cost['total_cost']:.6f}")
```


## Contributing

Contributions to TokenMeter are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

- This project uses pricing data from [LiteLLM](https://github.com/BerriAI/litellm).
- Token counting is performed using the [tiktoken](https://github.com/openai/tiktoken) library.

## Support

If you encounter any problems or have any questions, please open an issue on the [GitHub repository](https://github.com/rehan-ai/tokenmeter/issues).
python-upnp 1.0.0
UPnP library in pure python
Requires Python: >=3.10

# PyUPnP

UPnP library in pure python. Client and server. Typed.

## Installation

```shell
pip install python-upnp
```

## Usage

### CLI Client

```
$ pyupnp_client --help
usage: pyupnp_client [-h] [--user-agent USER_AGENT] [--friendly-name FRIENDLY_NAME] [--max-delay [1-5]]
                     [-L ADDRESS[%IFACE]] [-r ADDRESS[:PORT]] [-t TARGET] [--count COUNT]
                     [--interval INTERVAL] [-u UDN] [-d] [-i [UDN/...]SERVICE_ID/ACTION]
                     [-a ARG=VALUE [ARG=VALUE ...]] [--quiet]

options:
  -h, --help            show this help message and exit
  --user-agent USER_AGENT
                        Client user agent string.
  --friendly-name FRIENDLY_NAME
                        Client friendly name.
  --max-delay [1-5]     Maximum delay for multicast search in seconds.
  -L ADDRESS[%IFACE], --local-address ADDRESS[%IFACE]
                        Local address and interface. Can be specified multiple times.
  -r ADDRESS[:PORT], --remote-address ADDRESS[:PORT]
                        Remote address and port for unicast SSDP discovery.
  -t TARGET, --target TARGET
                        Discovery target.
  --count COUNT         Count of discovery requests to send.
  --interval INTERVAL   Interval between discovery requests.
  -u UDN, --udn UDN     Device UDN. Can be specified multiple times. If not specified, all devices will be processed.
  -d, --dump            Dump found devices information.
  -i [UDN/...]SERVICE_ID/ACTION, --invoke [UDN/...]SERVICE_ID/ACTION
                        Invoke action on specified service. Arguments are specified by --arg.
  -a ARG=VALUE [ARG=VALUE ...], --args ARG=VALUE [ARG=VALUE ...]
                        Arguments to pass to action. Used with --invoke.
  --quiet               Suppress console output.

```



## License

MIT License. Full test available in LICENSE file.
django-vendor-promo 0.2.12
Extension to Django Vendor to add Promo Code capabilities
Requires Python: >=3.7
Keywords: django, app

![Django Vendor Promo CI](https://github.com/renderbox/django-vendor-promo/workflows/Django%20Vendor%20Promo%20CI/badge.svg)

![Django Vendor Promo CI](https://github.com/renderbox/django-vendor-promo/workflows/Django%20Vendor%20Promo%20Develop/badge.svg)

# Django Vendor Promo

Extension to Django Vendor to add Promo Code capabilities
multicast-py 1.0.0
Low-to-mid level wrapper for multicast sockets
Requires Python: >=3.10

# multicast.py

Low-to-mid level wrapper for multicast sockets. Simple and cross-platform.

The library is typed, but if you want: [DOCS HERE](https://gitlab.neko-dev.ru/me/multicast-py/-/blob/main/DOCS.md).

## Installation

```sh
pip install multicast_py
```

## Usage 

Creation socket to send multicast packets:

```python
s = MulticastSocket()
s.multicast_ttl = 2  # if needed
s.multicast_loop = False  # if needed, POSIX only (on Windows does nothing)
s.set_multicast_if(address='192.168.0.101')  # local ip, used almost always

s.sendto(b'Multicast message', (group_address, group_port))  # send multicast message
```

Creation socket to receive multicast packets:

```python
s = MulticastSocket()
s.reuse_addr = True  # if needed
s.multicast_loop = False  # if needed, Windows only (on POSIX does nothing)
s.bind_multicast((group_address, group_port))
s.add_membership(group_address)  # also can specify local address

data = s.recvfrom(1024)  # receive multicast message
```

## License

MIT License. Full test available in LICENSE file.
automatexpertise 1.1.5
Utilitaire qui permet de d'automatiser l'initialisation d'AutomateXpertise
Requires Python: <4,>=3.9

### Utilisation du wrapper Python

#### Installation du wrapper

```
pip install automatexpertise
```

#### Créer un conteneur

```
automatexpertise create <cible/entreprise>
```

Veuillez donner un nom qui permettra d'identifier le conteneur. Votre conteneur aura comme nom final automatexpertise-<cible/entreprise>


Ex: Pour une utilisation hackthebox réaliser la commande `automatexpertise create hackthebox` ce qui donnera automatexpertise-hackthebox


#### Lister les conteneurs

```
automatexpertise list
```

#### Démarrer un conteneur éteint

```
automatexpertise start <nomduconteneur>
```

#### Arrêter un conteneur

```
automatexpertise stop <nomduconteneur>
```

#### Supprimer un conteneur

```
automatexpertise delete <nomduconteneur>
```
radix-heap 0.1.0
Monotone priority queue useful for shortest paths and more; wraps Rust's radix-heap crate
Requires Python: >=3.8

# Radix Heap for Python

This module provides a Python implementation of Radix Heap-like data structures, designed to function as monotone priority queues. The heavy-lifting for the heap operations is powered by the `radix-heap` Rust crate from [mpdn](https://github.com/mpdn/radix-heap) -- they did 99% of the hard work by implementing the data structure.

Monotone priority queues are useful for pathfinding in general (e.g., A*, Dijkstra's algorithm). A monotone priority queue must maintain the invariant where the extracted elements form a monotone sequence (e.g., for a max-heap, the extracted elements are in non-increasing order).

## Overview

A Radix Heap is a type of priority queue where the extracted elements form a monotone sequence. This module includes implementations for both maximum and minimum heaps for integer and float keys.

## Classes

- `RadixHeapLike[K, V]`: A generic base class for Radix Heap-like structures.
- `RadixMaxHeapInt[V]`: A radix max heap for integers.
- `RadixMinHeapInt[V]`: A radix min heap for integers.
- `RadixMaxHeap[V]`: A radix max heap for floats (or integers).
- `RadixMinHeap[V]`: A radix min heap for floats (or integers).

## Methods

- `push(key: K, value: V) -> None`: Adds a key-value pair to the heap.
- `pop() -> V`: Removes and returns the minimum value from the heap.
- `pop_with_key() -> tuple[K, V]`: Removes and returns the minimum key-value pair from the heap.
- `top() -> V | None`: Returns the minimum value from the heap without removing it.
- `clear() -> None`: Removes all elements from the heap.
- `__len__() -> int`: Returns the number of elements in the heap.

## Usage Examples

### Radix Max Heap for Integers

```python
from radix_heap import RadixMaxHeapInt

heap = RadixMaxHeapInt()
heap.push(10, "A")
heap.push(5, "B")
heap.push(8, "C")

while heap:
    print(heap.pop())
# Output: "A", "C", "B"
```

## Installation

To install the module, you can use pip:

```bash
pip install radix-heap
```

## Contributing

Contributions are welcome! Please check the repository and feel free to open issues or submit pull requests.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.