Here’s the enhanced and polished version of the entire `README.md` for your **LWM: Large Wireless Model** repository:

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# 📡 **LWM: Large Wireless Model**

Welcome to the **LWM** (Large Wireless Model) repository! This project hosts a pre-trained model designed to process and extract features from wireless communication datasets, specifically the **DeepMIMO** dataset. Follow the instructions below to clone the repository, load the data, and perform inference with LWM.

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## 🛠 **How to Use**

### 1. **Clone the Repository**

To get started, clone the Hugging Face repository to your local machine with the following Python code:

```python
import subprocess
import os
import sys
import importlib.util
import torch

# Hugging Face public repository URL
repo_url = "https://huggingface.co/sadjadalikhani/LWM"

# Directory where the repo will be cloned
clone_dir = "./LWM"

# Step 1: Clone the repository if it hasn't been cloned already
if not os.path.exists(clone_dir):
    print(f"Cloning repository from {repo_url} into {clone_dir}...")
    result = subprocess.run(["git", "clone", repo_url, clone_dir], capture_output=True, text=True)

    if result.returncode != 0:
        print(f"Error cloning repository: {result.stderr}")
        sys.exit(1)
    print(f"Repository cloned successfully into {clone_dir}")
else:
    print(f"Repository already cloned into {clone_dir}")

# Step 2: Add the cloned directory to Python path
sys.path.append(clone_dir)

# Step 3: Import necessary functions
def import_functions_from_file(module_name, file_path):
    try:
        spec = importlib.util.spec_from_file_location(module_name, file_path)
        module = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(module)

        for function_name in dir(module):
            if callable(getattr(module, function_name)) and not function_name.startswith("__"):
                globals()[function_name] = getattr(module, function_name)
        return module
    except FileNotFoundError:
        print(f"Error: {file_path} not found!")
        sys.exit(1)

# Step 4: Import functions from the repository
import_functions_from_file("lwm_model", os.path.join(clone_dir, "lwm_model.py"))
import_functions_from_file("inference", os.path.join(clone_dir, "inference.py"))
import_functions_from_file("load_data", os.path.join(clone_dir, "load_data.py"))
import_functions_from_file("input_preprocess", os.path.join(clone_dir, "input_preprocess.py"))
print("All required functions imported successfully.")
```

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### 2. **Load the LWM Model**

Once the repository is cloned, load the pre-trained **LWM** model using the following code:

```python
# Step 5: Load the LWM model (with flexibility for the device)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Loading the LWM model on {device}...")
model = LWM.from_pretrained(device=device)
```

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### 3. **Load the DeepMIMO Dataset**

Load the DeepMIMO dataset using the pre-defined loading function:

```python
# Step 6: Load dataset (direct call, no module prefix)
print("Loading DeepMIMO dataset...")
deepmimo_data = load_DeepMIMO_data()
```

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### 4. **Tokenize the DeepMIMO Dataset**

Tokenize the dataset based on specific scenarios from DeepMIMO. Below is a list of available scenarios and their links for more information:

| **Scenario**  | **City**      | **Link to DeepMIMO Page**                                                                                     |
|---------------|---------------|----------------------------------------------------------------------------------------------------------------|
| Scenario 0    | Denver        | [DeepMIMO City Scenario 18](https://www.deepmimo.net/scenarios/deepmimo-city-scenario18/)                      |
| Scenario 1    | Indianapolis  | [DeepMIMO City Scenario 15](https://www.deepmimo.net/scenarios/deepmimo-city-scenario15/)                      |
| Scenario 2    | Oklahoma      | [DeepMIMO City Scenario 19](https://www.deepmimo.net/scenarios/deepmimo-city-scenario19/)                      |
| Scenario 3    | Fort Worth    | [DeepMIMO City Scenario 12](https://www.deepmimo.net/scenarios/deepmimo-city-scenario12/)                      |
| Scenario 4    | Santa Clara   | [DeepMIMO City Scenario 11](https://www.deepmimo.net/scenarios/deepmimo-city-scenario11/)                      |
| Scenario 5    | San Diego     | [DeepMIMO City Scenario 7](https://www.deepmimo.net/scenarios/deepmimo-city-scenario7/)                        |

#### **Operational Settings**:
- **Antennas at BS**: 32
- **Antennas at UEs**: 1
- **Subcarriers**: 32
- **Paths**: 20

#### **Tokenization Code**:
Select and tokenize specific scenarios by adjusting the `scenario_idxs`. In the example below, we select the first two scenarios.

```python
# Step 7: Tokenize the dataset
scenario_idxs = torch.arange(2)  # Adjust the number of scenarios you want
print("Tokenizing the dataset...")
preprocessed_chs = tokenizer(deepmimo_data, scenario_idxs, gen_raw=True)
```

- The dataset will be tokenized according to the selected scenarios and preprocessing configurations.

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### 5. **LWM Inference**

Once the dataset is tokenized, generate either **raw channels** or the **inferred LWM embeddings** by choosing the input type.

```python
# Step 8: Generate the dataset for inference
input_type = ['cls_emb', 'channel_emb', 'raw'][1]  # Modify input type as needed
dataset = dataset_gen(preprocessed_chs, input_type, model)
```

You can choose between:
- `cls_emb`: LWM CLS token embeddings
- `channel_emb`: LWM channel embeddings
- `raw`: Raw wireless channel data

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## 🔄 **Post-processing for Downstream Task**

### 1. **Use the Dataset in Downstream Tasks**

Finally, use the generated dataset for your downstream tasks, such as classification, prediction, or analysis.

```python
# Step 9: Print results
print(f"Dataset generated with shape: {dataset.shape}")
print("Inference completed successfully.")
```

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## 📋 **Requirements**

- **Python 3.x**
- **PyTorch**