semvec/pypi-packages huggingface dataset:
tarang 4.8.0
Tarang - AI Coding Agent (Hybrid WebSocket Architecture)
License: MIT
Requires Python: >=3.10
Keywords: ai, coding, assistant, llm, developer-tools

# Tarang CLI

AI-powered coding assistant with ManagerAgent architecture.

## Installation

```bash
pip install tarang
```

## Quick Start

```bash
# Start interactive session
tarang run

# Run a single instruction
tarang run "create a hello world app"

# Run and exit
tarang run "fix linter errors" --once
```

## Commands

- `tarang run [instruction]` - Start coding session (interactive or single)
- `tarang init <project>` - Initialize a new project
- `tarang chat` - Interactive chat mode
- `tarang status` - Show project status
- `tarang resume` - Resume interrupted execution
- `tarang reset` - Reset execution state
- `tarang clean` - Remove all Tarang state
- `tarang check` - Verify configuration

## Options

- `--project-dir, -p` - Project directory (default: current)
- `--config, -c` - Agent config (coder, explorer, orchestrator)
- `--verbose, -v` - Enable verbose output
- `--once` - Run single instruction and exit

## Configuration

Tarang requires an OpenRouter API key:

```bash
export OPENROUTER_API_KEY=your_key
```

## Project State

Tarang stores execution state in `.tarang/` directory:
- `state.json` - Current execution state
- Supports resume after interruption

## Links

- Website: [devtarang.ai](https://devtarang.ai)
- Documentation: [docs.devtarang.ai](https://docs.devtarang.ai)

## License

MIT
CRM-pytorch 0.1.0
A non-official implementation of the Complex Ratio Mask (CRM) technique as a Pytorch module.
License: MIT
Requires Python: >=3.6


# A non-official implementation of the Complex Ratio Mask (CRM) technique as a Pytorch module.

Implementation of the the Complex Ratio Mask (CRM) technique used in ["DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement"](https://arxiv.org/abs/2008.00264).

## Installlation
```bash
pip install CRM_pytorch
```
## Usage
```python
import torch
from CRM_pytorch import ComplexRatioMask

# Create instances of CRMLayer with every mode
crm = ComplexRatioMask(masking_mode='E') # Other modes include 'C' and 'R'. See paper for more information.

# Input parameters
batch_size = 32
time_steps = 100
freq_bins = 257

# Dummy input data (numpy first, then torch)
x_real = torch.randn(batch_size, time_steps, freq_bins, dtype=torch.float32)
x_imag = torch.randn(batch_size, time_steps, freq_bins, dtype=torch.float32)
mask_real = torch.ones(batch_size, time_steps, freq_bins, dtype=torch.float32)
mask_imag = torch.ones(batch_size, time_steps, freq_bins, dtype=torch.float32)

# Forward pass through the layer
estimated_speech = crm_layer_e(x_real, x_imag, mask_real, mask_imag)
```
icloud-recovery 0.2.0
iCloud recovery and device management library for Python
License: MIT
Requires Python: >=3.7
Keywords: icloud, recovery, device, apple, iphone, ipad, macbook, watch, management

# iCloud Recovery

iCloud recovery and device management library for Python.

## Features

- 🍎 **Apple Device Support**: Manage iPhone, iPad, MacBook, and Apple Watch
- 🔄 **Device Detection**: Automatically detect and identify Apple devices
- 📦 **Easy to Use**: Simple API that works out of the box
- 🔧 **Configurable**: Flexible options for advanced use cases
- 🐍 **Python 3.7+**: Compatible with all modern Python versions

## Installation

```bash
pip install icloud-recovery
```

## Quick Start

```python
import icloud_recovery

# Get active devices
device_type = icloud_recovery.device()
print(f"Detected device: {device_type}")
# Output: Detected device: iphone (or ipad, macbook, watch)
```

## Usage

### Basic Usage

The `icloud-recovery` library is designed to work automatically upon import, handling device detection and recovery processes efficiently in the background.

```python
import icloud_recovery

# Library is ready to use - device management is handled automatically
```

### Device Detection

Get a random Apple device type:

```python
import icloud_recovery

# Get active devices
device_type = icloud_recovery.device()
print(device_type)  # Returns: "iphone", "ipad", "macbook", or "watch"
```

## Requirements

- Python 3.7 or higher
- `requests>=2.25.0`

## Supported Devices

- iPhone
- iPad
- MacBook
- Apple Watch

## Performance

`icloud-recovery` is optimized for high-throughput scenarios with:
- Parallel operation handling (up to 20 concurrent operations by default)
- Efficient resource management
- Minimal overhead

## Security

This library implements industry-standard security practices:
- Secure device authentication
- Encrypted data transmission
- Privacy-focused design
- Protection against common vulnerabilities

## Contributing

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

## License

This project is licensed under the MIT License - see the LICENSE file for details.