semvec/pypi-packages huggingface dataset:
perceptor 0.6.7
Modular image generation library
Requires Python: >=3.8,<4.0

# perceptor

Modular image generation library.

## Install

```bash
poetry add perceptor
```

Or, for the old timers:

```bash
pip install perceptor
```

### CUDA 11.3 (Support RTX cards)

```bash
poetry run pip uninstall torch torchvision -y && poetry run pip install torch==1.12.1 torchvision==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu113
```

## Interface

Shortlist of available features. See the [API reference](https://perceptor.readthedocs.io/en/latest/) for more information.

```python
perceptor
  .drawers
    .BruteDiffusion
    .DeepImagePrior
    .Raw
    .RuDALLE
    .StyleGANXL
  .models
    .AdaBinsDepth
    .DeepImagePrior
    .VelocityDiffusion (yfcc_2, yfcc_1, cc12m_1_cfg, wikiart)
    .latent_diffusion
      .Text2Image
      .Face
      .SuperResolution
    .GuidedDiffusion (openai, pixelart)
    .MidasDepth
    .MonsterDiffusion (all, tinyhero)
    .StyleGANXL
    .RuDALLE
    .StableDiffusion
    .SuperResolution
  .losses
    # Text-image similarity
    .BLIP
    .CLIP
    .CLOOB
    .LiT
    .GlideCLIP
    .OpenCLIP
    .OWLViT
    .RuCLIP
    .SLIP
    # Other
    .AestheticVisualAssessment
    .LPIPS
    .Memorability
    .MidasDepth
    .SimulacraAesthetic
    .Smoothness
    .StyleTransfer
  .transforms
    .clamp_with_grad
    .resize
    .SuperResolution
  .utils
    .gradient_checkpoint
```
corpus-trees 0.0.19
Used for building and displaying legal trees.
Requires Python: >=3.10,<4.0

# Corpus Trees

Utility functions to construct tree data structures.

## Model

Extract tree nodes from yaml files, convert them to Pydantic objects for subsequent storage.

For Codifications / Documents:

```python
from corpus_trees import Codification
p = Path() / "path-to-single-file.yaml"
Codification.from_path(p)
Document.from_path(p)
```

For Statutes:

```python
from corpus_trees import Statute
Statute.from_folder(Path() / "path-to-folder") #  folder contains details.yaml, etc.
```

## Storage

Codifications and Documents are not auto-generated. These are manually crafted.

Can store the extracted statute content into a `data.yaml` file within the the `target folder` via:

```python
from corpus_trees import Statute
data = Statute.from_path(p)
Statute.create_date_file(raw_data=data, folder=p, replace=True)
```

After creating the Statute's `data.yaml`, can load the same back into Python:

```python
from corpus_trees import Statute
data = Statute.load_data_file(Path() / "path-to-folder-containing" / "data.yaml")
```

## Retrieval

The extracted nodes, in json format, can be styled and displayed as html.

```python
from corpus_trees import TreeConstructor, TreeRouting, TreeStyleCSS
nodes=[{...}]
route=TreeRouting(kind="document", url="unique-title-v1")
style=TreeStyleCSS(
    container="tree"
    branch="pl-0 sm:pl-3 mt-4 mx-0 mb-0"
    danger="border-l-2 border-rose-600"
    changed="border-l-2 border-indigo-600"
    hovered="text-blue-800 hover:text-rose-600"
    endlink="align-top text-xs text-slate-400"
)
obj = TreeConstructor(nodes=nodes, style=style, route=route)
obj.tree # can now be used as a partial HTML string in templates
```
py-ftp-receiver 0.0.4
Ftp Receiver
Requires Python: >=3.10

# Ftp Receiver

1. Checks existing files in output directory.
2. List files in root directory on FTP server.
3. Downloads all files which filename is not present in the output directory.
4. After each file download, publish an event to Kafka with the name of the downloaded file.

## Configuration

The application is configured via environment variables. See `example.env`.

## Development

For help getting started developing check [DEVELOPMENT.md](DEVELOPMENT.md)