### Install PyTorch and GPyTorch Dependencies

Source: https://github.com/panaali/word2ket/blob/master/README.md

Installs necessary deep learning libraries, PyTorch and GPyTorch, using the conda package manager. These are prerequisites for running word2ket examples.

```bash
# Install PyTorch
conda install pytorch torchvision -c pytorch
# Install GPyTorch
conda install gpytorch -c gpytorch
```

--------------------------------

### Install word2ket Package

Source: https://github.com/panaali/word2ket/blob/master/README.md

Installs the word2ket library using pip, making its embedding layers and utility functions available for use in PyTorch projects.

```bash
pip install word2ket
```

--------------------------------

### Run Text Summarization Example

Source: https://github.com/panaali/word2ket/blob/master/README.md

Executes a text summarization task using the GIGAWORD dataset. It provides commands to run the task with standard PyTorch `nn.Embedding`, `EmbeddingKet`, and `EmbeddingKetXS` layers, allowing for comparison of performance and resource usage.

```bash
# Using Pytorch nn.Embedding Layer
python seq2seq_attn.py --embedding_type nn.Embedding --gpu 4 --runName G_000 --config-model config_model --config-data config_giga

# Using EmbeddingKet Layer
python seq2seq_attn.py --embedding_type EmbeddingKet   --gpu 0 --runName V2K_G_000       --config-model config_model --config-data config_giga --order 4 --rank 1

# Using EmbeddingKetXS Layer
python seq2seq_attn.py --embedding_type EmbeddingKetXS --gpu 0 --runName V2K_XS_G_000 --config-model config_model --config-data config_giga --order 4 --rank 1
```

--------------------------------

### Run German-English Machine Translation Example

Source: https://github.com/panaali/word2ket/blob/master/README.md

Sets up and runs a German-English machine translation task using the IWSLT2014 dataset. This example showcases the application of word2ket's embedding layers in a sequence-to-sequence model for translation.

```bash
# Install Texar-PyTorch
pip install texar-pytorch
# Install Rouge
pip install rouge

cd ./examples/texar/ 
python ./prepare_data.py --data iwslt2014
```

--------------------------------

### Using EmbeddingKetXS Layer in PyTorch

Source: https://github.com/panaali/word2ket/blob/master/README.md

Demonstrates the creation and usage of the `EmbeddingKetXS` layer from the word2ket library. It shows how to initialize the layer and uses the `summary` function to display its parameter count, highlighting its space efficiency compared to standard PyTorch `nn.Embedding`.

```python
# examples/demo.py 
from word2ket import EmbeddingKet, EmbeddingKetXS, ketify, summary
from torch import nn
import logging
logging.basicConfig(level=logging.INFO)


# Word2Ket Embedding Layer
w2v_embedding_layer = EmbeddingKetXS(num_embeddings=30000, embedding_dim=256, order=4, rank=1)
summary(w2v_embedding_layer)
"""
INFO:root:EmbeddingKetXS num_embeddings_leaf: 14
INFO:root:EmbeddingKetXS embedding_dim_leaf: 4
INFO:root:EmbeddingKetXS weight_leafs shape: torch.Size([4, 1, 14, 4])
Module Name                                                                           Total Parameters  Trainable Parameters # Elements in Trainable Parametrs       
EmbeddingKetXS(30000, 256)                                                            1                 1                    224                                     
Total number of trainable parameters elements 224
"""

# PyTorch Embedding Layer
class MyModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim):
        super(MyModel, self).__init__()
        self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=embedding_dim)
    
    def forward(self, x):
        self.embedding()
model = MyModel(vocab_size=30000, embedding_dim=256)
print("embedding.weight.shape: ", model.embedding.weight.shape)
"""
embedding.weight.shape:  torch.Size([30000, 256])
"""
summary(model)
"""
Module Name                                                                           Total Parameters  Trainable Parameters # Elements in Trainable Parametrs       
Embedding(30000, 256)                                                                 1                 1                    7,680,000                               
Total number of trainable parameters elements 7,680,000
"""

# Replace the nn.Embedding to EmbeddingKetXS automatically using the ketify function. 
ketify(model, order=4, rank=2, use_EmbeddingKetXS=True)
summary(model)
"""
INFO:root:EmbeddingKetXS num_embeddings_leaf: 14
INFO:root:EmbeddingKetXS embedding_dim_leaf: 4
INFO:root:EmbeddingKetXS weight_leafs shape: torch.Size([4, 2, 14, 4])
INFO:root:Replaced embedding in MyModel
Module Name                                                                           Total Parameters  Trainable Parameters # Elements in Trainable Parametrs       
EmbeddingKetXS(30000, 256)                                                            1                 1                    448                                     
Total number of trainable parameters elements 448
"""

```

--------------------------------

### Run seq2seq_attn with EmbeddingKet

Source: https://github.com/panaali/word2ket/blob/master/README.md

Executes the seq2seq_attn.py script using the EmbeddingKet layer. This command includes additional parameters for order and rank, specifying the embedding type, GPU, run name, and configuration files.

```python
python seq2seq_attn.py --embedding_type EmbeddingKet --gpu 0 --runName V2K_I_000 --config-model config_model --config-data config_iwslt14 --order 4 --rank 1
```

--------------------------------

### Run seq2seq_attn with EmbeddingKetXS

Source: https://github.com/panaali/word2ket/blob/master/README.md

Executes the seq2seq_attn.py script using the EmbeddingKetXS layer. This command specifies the embedding type, GPU, run name, configuration files, and additional parameters for order and rank.

```python
python seq2seq_attn.py --embedding_type EmbeddingKetXS --gpu 0 --runName V2K_XS_I_000 --config-model config_model --config-data config_iwslt14 --order 4 --rank 1
```

--------------------------------

### Run seq2seq_attn with nn.Embedding

Source: https://github.com/panaali/word2ket/blob/master/README.md

Executes the seq2seq_attn.py script using PyTorch's nn.Embedding layer. This command specifies the embedding type, GPU to use, a run name, and configuration files for the model and data.

```python
python seq2seq_attn.py --embedding_type nn.Embedding --gpu 4 --runName I_000 --config-model config_model --config-data config_iwslt14
```

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