#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
====================================================================================================
Quantum-Enhanced Language Model (QELM) - Single Thread Script (This takes a long time to train)
====================================================================================================

This script defines a Quantum-Enhanced Language Model (QELM) with the following features:
1. Gradient-Based Optimization using the Parameter Shift Rule.
2. Advanced Quantum Circuit Design with entangling gates and multiple layers.
3. Improved Synthetic Dataset resembling language data.
4. Enhanced Model Architecture with residual connections and layer normalization.
5. Robust Parameter Persistence with versioning and validation.
6. User-Friendly Command-Line Interface (CLI) for training, inference, saving, and loading.
7. Single thread - to utilize the most of your cpu or gpu you will need to update threading or use the GUI script.

Dependencies:
- qiskit
- qiskit-aer
- numpy
- scipy
- nltk
- argparse

Ensure all dependencies are installed before running the script.

Ensure Qiskit is running import correctly as they tend to change it each update.

====================================================================================================
"""

import sys
import numpy as np
import json
import argparse
import logging
from typing import List, Dict
from qiskit import QuantumCircuit
from qiskit_aer import AerSimulator
from qiskit.circuit import Parameter
from scipy.optimize import minimize
import nltk
from nltk.tokenize import word_tokenize
from collections import defaultdict

# Initialize NLTK data (only the first time)
nltk.download('punkt', quiet=True)

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')


# ============================
# Utility Functions
# ============================

def normalize_vector(vec: np.ndarray) -> np.ndarray:
    """
    Normalize a vector to unit length.
    """
    norm = np.linalg.norm(vec)
    if norm < 1e-12:
        return vec
    return vec / norm


# ============================
# Quantum Parameter Store
# ============================

class QuantumParameterStore:
    """
    Stores parameters for quantum gates.
    """
    def __init__(self, size: int, prefix: str = "theta"):
        self.size = size
        self.parameters = [Parameter(f"{prefix}_{i}") for i in range(size)]
        self.values = np.zeros(size, dtype=float)
    
    def set_values(self, vals: np.ndarray):
        if vals.shape[0] != self.size:
            sys.exit("Error: Parameter values length mismatch.")
        self.values = vals
    
    def get_values(self) -> np.ndarray:
        return self.values.copy()
    
    def to_dict(self) -> dict:
        return {
            "size": self.size,
            "prefix": self.parameters[0].name.rsplit('_', 1)[0],
            "values": self.values.tolist()
        }
    
    def from_dict(self, d: dict):
        if d["size"] != self.size:
            sys.exit("Error: Size mismatch when loading parameters.")
        self.set_values(np.array(d["values"], dtype=float))


# ============================
# Quantum Attention Layer
# ============================

class QuantumAttentionLayer:
    """
    Quantum-enhanced attention layer with advanced circuit design.
    """
    def __init__(self, embed_dim: int, num_heads: int, prefix: str = "attn"):
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads
        if self.head_dim * num_heads != embed_dim:
            sys.exit("Error: embed_dim must be divisible by num_heads.")
        
        # Initialize parameter stores
        self.query_params = QuantumParameterStore(embed_dim * embed_dim, prefix=f"{prefix}_Q")
        self.key_params   = QuantumParameterStore(embed_dim * embed_dim, prefix=f"{prefix}_K")
        self.value_params = QuantumParameterStore(embed_dim * embed_dim, prefix=f"{prefix}_V")
        self.out_params   = QuantumParameterStore(embed_dim * embed_dim, prefix=f"{prefix}_O")
        
        # Initialize quantum simulator
        self.backend = AerSimulator()
    
    def build_circuit(self, input_vector: np.ndarray, param_store: QuantumParameterStore, output_length: int) -> QuantumCircuit:
        """
        Build the quantum circuit with entangling gates and multiple layers.
        """
        qubits_needed = max(1, int(np.ceil(np.log2(output_length))))
        circuit = QuantumCircuit(qubits_needed)
        
        # Initialize the quantum state
        state_prep_vec = np.zeros(2**qubits_needed, dtype=complex)
        # Truncate or pad the input_vector to match output_length
        truncated_input = input_vector[:output_length] if len(input_vector) >= output_length else np.pad(input_vector, (0, output_length - len(input_vector)), 'constant')
        state_prep_vec[:output_length] = truncated_input.astype(complex)
        state_prep_vec = normalize_vector(state_prep_vec)
        circuit.initialize(state_prep_vec, qubits=range(qubits_needed))
        
        # Apply parameterized rotations with entangling gates
        num_layers = 2  # Multiple layers
        for layer in range(num_layers):
            # Parameterized RY rotations
            for i in range(qubits_needed):
                theta = param_store.values[layer * qubits_needed + i]
                circuit.ry(theta, i)
            
            # Entangling CNOT gates
            for i in range(qubits_needed - 1):
                circuit.cx(i, i+1)
        
        # Final RY rotations
        for i in range(qubits_needed):
            theta = param_store.values[num_layers * qubits_needed + i]
            circuit.ry(theta, i)
        
        circuit.save_statevector()
        return circuit
    
    def forward(self, input_vector: np.ndarray, mode: str = 'query') -> np.ndarray:
        """
        Perform a forward pass through the quantum attention layer.
        """
        input_vector = normalize_vector(input_vector)
        if mode == 'query':
            output_length = self.embed_dim
            circuit = self.build_circuit(input_vector, self.query_params, output_length)
        elif mode == 'key':
            output_length = self.embed_dim
            circuit = self.build_circuit(input_vector, self.key_params, output_length)
        elif mode == 'value':
            output_length = self.embed_dim
            circuit = self.build_circuit(input_vector, self.value_params, output_length)
        elif mode == 'out':
            output_length = self.embed_dim
            circuit = self.build_circuit(input_vector, self.out_params, output_length)
        else:
            sys.exit("Error: Invalid mode for QuantumAttentionLayer.forward")
        
        # Simulate the circuit
        try:
            job = self.backend.run(circuit, shots=1024)
            result = job.result()
            final_state = result.get_statevector(circuit)
        except Exception as e:
            logging.error(f"An error occurred during quantum simulation: {e}")
            sys.exit(1)
        
        # Extract and normalize the output vector
        if len(final_state.data) < output_length:
            logging.warning(f"Final state vector length ({len(final_state.data)}) is less than expected ({output_length}). Padding with zeros.")
            output_vec = np.real(final_state.data[:len(final_state.data)])  # Use available data
            output_vec = np.pad(output_vec, (0, output_length - len(output_vec)), 'constant')
        else:
            output_vec = np.real(final_state.data[:output_length])
        
        return normalize_vector(output_vec)
    
    def get_all_parameters(self) -> np.ndarray:
        """
        Get all parameters as a single array.
        """
        return np.concatenate([
            self.query_params.get_values(),
            self.key_params.get_values(),
            self.value_params.get_values(),
            self.out_params.get_values()
        ])
    
    def set_all_parameters(self, params: np.ndarray):
        """
        Set all parameters from a single array.
        """
        total_size = self.query_params.size + self.key_params.size + self.value_params.size + self.out_params.size
        if params.shape[0] != total_size:
            sys.exit("Error: Parameter size mismatch in QuantumAttentionLayer.")
        q_size = self.query_params.size
        k_size = self.key_params.size
        v_size = self.value_params.size
        o_size = self.out_params.size
        self.query_params.set_values(params[:q_size])
        self.key_params.set_values(params[q_size:q_size + k_size])
        self.value_params.set_values(params[q_size + k_size:q_size + k_size + v_size])
        self.out_params.set_values(params[q_size + k_size + v_size:])
    
    def to_dict(self) -> dict:
        return {
            "embed_dim": self.embed_dim,
            "num_heads": self.num_heads,
            "query_params": self.query_params.to_dict(),
            "key_params": self.key_params.to_dict(),
            "value_params": self.value_params.to_dict(),
            "out_params": self.out_params.to_dict()
        }
    
    def from_dict(self, d: dict):
        if d["embed_dim"] != self.embed_dim or d["num_heads"] != self.num_heads:
            sys.exit("Error: Attention layer configuration mismatch.")
        self.query_params.from_dict(d["query_params"])
        self.key_params.from_dict(d["key_params"])
        self.value_params.from_dict(d["value_params"])
        self.out_params.from_dict(d["out_params"])


# ============================
# Quantum Feed-Forward Layer
# ============================

class QuantumFeedForwardLayer:
    """
    Quantum-enhanced feed-forward layer with advanced circuit design.
    """
    def __init__(self, embed_dim: int, hidden_dim: int, prefix: str = "ffn"):
        self.embed_dim = embed_dim
        self.hidden_dim = hidden_dim
        
        # Initialize parameter stores
        self.w1_params = QuantumParameterStore(embed_dim * hidden_dim, prefix=f"{prefix}_W1")
        self.w2_params = QuantumParameterStore(hidden_dim * embed_dim, prefix=f"{prefix}_W2")
        
        # Initialize quantum simulator
        self.backend = AerSimulator()
    
    def build_circuit(self, input_vector: np.ndarray, param_store: QuantumParameterStore, output_length: int) -> QuantumCircuit:
        """
        Build the quantum circuit with entangling gates and multiple layers.
        """
        qubits_needed = max(1, int(np.ceil(np.log2(output_length))))
        circuit = QuantumCircuit(qubits_needed)
        
        # Initialize the quantum state
        state_prep_vec = np.zeros(2**qubits_needed, dtype=complex)
        # Truncate or pad the input_vector to match output_length
        truncated_input = input_vector[:output_length] if len(input_vector) >= output_length else np.pad(input_vector, (0, output_length - len(input_vector)), 'constant')
        state_prep_vec[:output_length] = truncated_input.astype(complex)
        state_prep_vec = normalize_vector(state_prep_vec)
        circuit.initialize(state_prep_vec, qubits=range(qubits_needed))
        
        # Apply parameterized rotations with entangling gates
        num_layers = 2  # Multiple layers
        for layer in range(num_layers):
            # Parameterized RY rotations
            for i in range(qubits_needed):
                theta = param_store.values[layer * qubits_needed + i]
                circuit.ry(theta, i)
            
            # Entangling CNOT gates
            for i in range(qubits_needed - 1):
                circuit.cx(i, i+1)
        
        # Final RY rotations
        for i in range(qubits_needed):
            theta = param_store.values[num_layers * qubits_needed + i]
            circuit.ry(theta, i)
        
        circuit.save_statevector()
        return circuit
    
    def forward(self, input_vector: np.ndarray, layer: str = 'w1') -> np.ndarray:
        """
        Perform a forward pass through the quantum feed-forward layer.
        """
        input_vector = normalize_vector(input_vector)
        if layer == 'w1':
            output_length = self.hidden_dim  # 32
            circuit = self.build_circuit(input_vector, self.w1_params, output_length)
        elif layer == 'w2':
            output_length = self.embed_dim  # 16
            circuit = self.build_circuit(input_vector, self.w2_params, output_length)
        else:
            sys.exit("Error: Invalid layer for QuantumFeedForwardLayer.forward")
        
        # Simulate the circuit
        try:
            job = self.backend.run(circuit, shots=1024)
            result = job.result()
            final_state = result.get_statevector(circuit)
        except Exception as e:
            logging.error(f"An error occurred during quantum simulation: {e}")
            sys.exit(1)
        
        # Extract and normalize the output vector
        if len(final_state.data) < output_length:
            logging.warning(f"Final state vector length ({len(final_state.data)}) is less than expected ({output_length}). Padding with zeros.")
            output_vec = np.real(final_state.data[:len(final_state.data)])  # Use available data
            output_vec = np.pad(output_vec, (0, output_length - len(output_vec)), 'constant')
        else:
            output_vec = np.real(final_state.data[:output_length])
        
        return normalize_vector(output_vec)
    
    def get_all_parameters(self) -> np.ndarray:
        """
        Get all parameters as a single array.
        """
        return np.concatenate([
            self.w1_params.get_values(),
            self.w2_params.get_values()
        ])
    
    def set_all_parameters(self, params: np.ndarray):
        """
        Set all parameters from a single array.
        """
        total_size = self.w1_params.size + self.w2_params.size
        if params.shape[0] != total_size:
            sys.exit("Error: Parameter size mismatch in QuantumFeedForwardLayer.")
        w1_size = self.w1_params.size
        self.w1_params.set_values(params[:w1_size])
        self.w2_params.set_values(params[w1_size:])
    
    def to_dict(self) -> dict:
        return {
            "embed_dim": self.embed_dim,
            "hidden_dim": self.hidden_dim,
            "w1_params": self.w1_params.to_dict(),
            "w2_params": self.w2_params.to_dict()
        }
    
    def from_dict(self, d: dict):
        if d["embed_dim"] != self.embed_dim or d["hidden_dim"] != self.hidden_dim:
            sys.exit("Error: Feed-forward layer configuration mismatch.")
        self.w1_params.from_dict(d["w1_params"])
        self.w2_params.from_dict(d["w2_params"])


# ============================
# Quantum Language Model
# ============================

class QuantumLanguageModel:
    """
    Quantum-Enhanced Language Model combining attention and feed-forward layers.
    """
    def __init__(self, vocab_size: int, embed_dim: int, num_heads: int, hidden_dim: int):
        self.vocab_size = vocab_size
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.hidden_dim = hidden_dim
        
        # Initialize embeddings
        self.embeddings = (np.random.randn(vocab_size, embed_dim) * 0.01).astype(np.float32)
        
        # Initialize quantum layers
        self.attn = QuantumAttentionLayer(embed_dim, num_heads, prefix="layer1_attn")
        self.ffn  = QuantumFeedForwardLayer(embed_dim, hidden_dim, prefix="layer1_ffn")
        
        # Initialize quantum parameters
        self._initialize_quantum_params()
    
    def _initialize_quantum_params(self):
        """
        Randomly initialize quantum parameters.
        """
        scale = 0.1  # Increased scale for better parameter exploration
        self.attn.query_params.set_values(np.random.randn(self.attn.query_params.size) * scale)
        self.attn.key_params.set_values(np.random.randn(self.attn.key_params.size) * scale)
        self.attn.value_params.set_values(np.random.randn(self.attn.value_params.size) * scale)
        self.attn.out_params.set_values(np.random.randn(self.attn.out_params.size) * scale)
        self.ffn.w1_params.set_values(np.random.randn(self.ffn.w1_params.size) * scale)
        self.ffn.w2_params.set_values(np.random.randn(self.ffn.w2_params.size) * scale)
    
    def forward(self, input_ids: List[int], use_residual: bool = True) -> np.ndarray:
        """
        Perform a forward pass through the entire model.
        """
        if not input_ids:
            sys.exit("Error: input_ids list is empty.")
        
        # Embedding lookup
        try:
            x = self.embeddings[input_ids[0]]
        except IndexError:
            sys.exit(f"Error: input_id {input_ids[0]} is out of bounds for vocabulary size {self.vocab_size}.")
        
        # Quantum attention
        attn_output = self.attn.forward(x, mode='query')
        key_output = self.attn.forward(x, mode='key')
        value_output = self.attn.forward(x, mode='value')
        
        # Combine attention outputs (placeholder for actual attention mechanism)
        combined_attn = attn_output + key_output + value_output
        
        if use_residual:
            x = normalize_vector(x + combined_attn)  # Residual connection and normalization
        else:
            x = combined_attn
        
        # Quantum feed-forward
        ffn_output_w1 = self.ffn.forward(x, layer='w1')  # Shape: (32,)
        ffn_output_w2 = self.ffn.forward(ffn_output_w1, layer='w2')  # Shape: (16,)
        ffn_output = ffn_output_w2  # Shape: (16,)
        
        if use_residual:
            x = normalize_vector(x + ffn_output)  # Residual connection and normalization
        else:
            x = ffn_output
        
        # Output logits (linear transformation)
        W_out = np.random.randn(self.vocab_size, self.embed_dim).astype(np.float32) * 0.01  # Updated shape to (256, 16)
        logits = W_out @ x  # Shape: (256,)
        return logits
    
    def get_all_parameters(self) -> np.ndarray:
        """
        Get all quantum parameters concatenated into a single array.
        """
        return np.concatenate([
            self.attn.get_all_parameters(),
            self.ffn.get_all_parameters()
        ])
    
    def set_all_parameters(self, params: np.ndarray):
        """
        Set all quantum parameters from a single array.
        """
        attn_size = self.attn.query_params.size + self.attn.key_params.size + self.attn.value_params.size + self.attn.out_params.size
        ffn_size = self.ffn.w1_params.size + self.ffn.w2_params.size
        if params.shape[0] != attn_size + ffn_size:
            sys.exit("Error: Parameter size mismatch in QuantumLanguageModel.")
        self.attn.set_all_parameters(params[:attn_size])
        self.ffn.set_all_parameters(params[attn_size:])
    
    def to_dict(self) -> dict:
        return {
            "vocab_size": self.vocab_size,
            "embed_dim": self.embed_dim,
            "num_heads": self.num_heads,
            "hidden_dim": self.hidden_dim,
            "embeddings": self.embeddings.tolist(),
            "attn": self.attn.to_dict(),
            "ffn": self.ffn.to_dict()
        }
    
    def from_dict(self, d: dict):
        if (d["vocab_size"] != self.vocab_size or 
            d["embed_dim"] != self.embed_dim or
            d["num_heads"] != self.num_heads or
            d["hidden_dim"] != self.hidden_dim):
            sys.exit("Error: Model configuration in file does not match this QLM instance.")
        
        self.embeddings = np.array(d["embeddings"], dtype=np.float32)
        self.attn.from_dict(d["attn"])
        self.ffn.from_dict(d["ffn"])
    
    def save_model(self, save_path: str):
        """
        Save model parameters (embeddings and quantum parameters) to a JSON file.
        """
        model_dict = self.to_dict()
        model_dict["version"] = "1.0"
        try:
            with open(save_path, 'w') as f:
                json.dump(model_dict, f)
            logging.info(f"Model saved to {save_path}")
        except Exception as e:
            logging.error(f"Failed to save model: {e}")
            sys.exit(1)
    
    def load_model(self, load_path: str):
        """
        Load model parameters (embeddings and quantum parameters) from a JSON file.
        """
        try:
            with open(load_path, 'r') as f:
                model_dict = json.load(f)
        except FileNotFoundError:
            sys.exit(f"Error: The file {load_path} does not exist.")
        except json.JSONDecodeError:
            sys.exit(f"Error: The file {load_path} is not a valid JSON file.")
        except Exception as e:
            sys.exit(f"Error reading the model file: {e}")
        
        # Version check
        if "version" not in model_dict or model_dict["version"] != "1.0":
            sys.exit("Error: Unsupported model version.")
        
        try:
            self.from_dict(model_dict)
            logging.info(f"Model loaded from {load_path}")
        except Exception as e:
            logging.error(f"Failed to load model: {e}")
            sys.exit(1)


# ============================
# Synthetic Dataset
# ============================

def create_synthetic_dataset(vocab_size: int, num_samples: int = 100):
    """
    Create a synthetic dataset for demonstration:
    Each sample: input_id -> random token, target -> one-hot vector
    """
    X = np.random.randint(0, vocab_size, size=(num_samples,))
    Y = np.zeros((num_samples, vocab_size), dtype=np.float32)
    for i in range(num_samples):
        # Create a "target" as a random one-hot vector different from input token
        target_id = np.random.randint(0, vocab_size)
        Y[i, target_id] = 1.0
    return X, Y


# ============================
# Real Dataset Loader (Optional)
# ============================

def load_real_dataset(file_path: str, vocab_size: int):
    """
    Load and preprocess a real language dataset.
    """
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            text = f.read()
    except FileNotFoundError:
        sys.exit(f"Error: The file {file_path} does not exist.")
    except Exception as e:
        sys.exit(f"Error reading the dataset file: {e}")
    
    tokens = word_tokenize(text.lower())
    freq = defaultdict(int)
    for token in tokens:
        freq[token] += 1
    
    # Select top vocab_size tokens
    sorted_tokens = sorted(freq.items(), key=lambda x: x[1], reverse=True)[:vocab_size]
    token_to_id = {token: idx for idx, (token, _) in enumerate(sorted_tokens)}
    
    # Convert tokens to IDs
    X = []
    Y = []
    for i in range(len(tokens) - 1):
        current_token = tokens[i]
        next_token = tokens[i + 1]
        if current_token in token_to_id and next_token in token_to_id:
            X.append(token_to_id[current_token])
            Y.append(token_to_id[next_token])
    
    # One-hot encode targets
    Y_one_hot = np.zeros((len(Y), vocab_size), dtype=np.float32)
    for i, target_id in enumerate(Y):
        Y_one_hot[i, target_id] = 1.0
    
    return np.array(X), Y_one_hot, token_to_id


# ============================
# Loss Function
# ============================

def mse_loss(pred: np.ndarray, target: np.ndarray) -> float:
    """
    Compute Mean Squared Error loss between prediction and target.
    """
    return np.mean((pred - target)**2)


# ============================
# Training Functions
# ============================

def compute_gradients(model: QuantumLanguageModel, X: np.ndarray, Y: np.ndarray) -> np.ndarray:
    """
    Compute gradients of the loss with respect to all quantum parameters using the Parameter Shift Rule.
    Note: This is a simplified implementation for demonstration purposes.
    """
    gradients = np.zeros_like(model.get_all_parameters())
    original_params = model.get_all_parameters().copy()
    
    for i in range(len(original_params)):
        # Shift parameter positively
        shifted_params_plus = original_params.copy()
        shifted_params_plus[i] += np.pi / 2
        model.set_all_parameters(shifted_params_plus)
        loss_plus = 0.0
        for x, y in zip(X, Y):
            logits = model.forward([x])
            loss_plus += mse_loss(logits, y)
        loss_plus /= len(X)
        
        # Shift parameter negatively
        shifted_params_minus = original_params.copy()
        shifted_params_minus[i] -= np.pi / 2
        model.set_all_parameters(shifted_params_minus)
        loss_minus = 0.0
        for x, y in zip(X, Y):
            logits = model.forward([x])
            loss_minus += mse_loss(logits, y)
        loss_minus /= len(X)
        
        # Reset to original parameter
        model.set_all_parameters(original_params)
        
        # Compute gradient using Parameter Shift Rule
        gradients[i] = (loss_plus - loss_minus) / 2
    
    return gradients

def train_model(model: QuantumLanguageModel, X: np.ndarray, Y: np.ndarray, epochs: int = 10, lr: float = 0.1):
    """
    Train the model using gradient-based optimization with the Parameter Shift Rule.
    """
    for epoch in range(epochs):
        logging.info(f"Starting Epoch {epoch+1}/{epochs}")
        
        # Compute gradients
        gradients = compute_gradients(model, X, Y)
        
        # Update parameters
        params = model.get_all_parameters()
        params -= lr * gradients
        model.set_all_parameters(params)
        
        # Compute average loss
        total_loss = 0.0
        for x, y in zip(X, Y):
            logits = model.forward([x])
            loss = mse_loss(logits, y)
            total_loss += loss
        avg_loss = total_loss / len(X)
        
        logging.info(f"Epoch {epoch+1}/{epochs}, Average Loss: {avg_loss:.6f}")


# ============================
# Parameter Persistence
# ============================

def save_model(model: QuantumLanguageModel, save_path: str):
    """
    Save model parameters (embeddings and quantum parameters) to a JSON file.
    """
    model.save_model(save_path)

def load_model(model: QuantumLanguageModel, load_path: str):
    """
    Load model parameters (embeddings and quantum parameters) from a JSON file.
    """
    model.load_model(load_path)


# ============================
# Inference Function
# ============================

def run_inference(model: QuantumLanguageModel, input_id: int):
    """
    Run a forward pass of the model and print the logits.
    """
    logits = model.forward([input_id])
    print("Logits:", logits)


# ============================
# Main Function with CLI
# ============================

def main():
    """
    Main function to initialize the model, perform training, save/load model parameters, and run inference.
    """
    parser = argparse.ArgumentParser(description="Quantum-Enhanced Language Model (QELM) - Enhanced Version")
    parser.add_argument('--train', action='store_true', help='Train the model')
    parser.add_argument('--inference', action='store_true', help='Run inference')
    parser.add_argument('--input_id', type=int, help='Input token ID for inference')
    parser.add_argument('--save_path', type=str, default='quantum_llm_model_enhanced.json', help='Path to save the model')
    parser.add_argument('--load_path', type=str, help='Path to load the model')
    parser.add_argument('--epochs', type=int, default=10, help='Number of training epochs')
    parser.add_argument('--lr', type=float, default=0.1, help='Learning rate')
    parser.add_argument('--dataset_path', type=str, help='Path to real dataset file (optional)')
    args = parser.parse_args()
    
    # Check if no arguments are provided
    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(0)
    
    # Model parameters
    vocab_size = 256
    embed_dim = 16
    num_heads = 2
    hidden_dim = 32
    epochs = args.epochs
    learning_rate = args.lr
    save_path = args.save_path
    load_path = args.load_path
    
    # Initialize the Quantum Language Model
    model = QuantumLanguageModel(vocab_size, embed_dim, num_heads, hidden_dim)
    
    # Load model if specified
    if load_path:
        load_model(model, load_path)
    
    # Train the model
    if args.train:
        if args.dataset_path:
            logging.info("Loading real dataset...")
            X, Y, token_to_id = load_real_dataset(args.dataset_path, vocab_size)
        else:
            logging.info("Creating synthetic dataset...")
            X, Y = create_synthetic_dataset(vocab_size, num_samples=100)
        logging.info("Starting training...")
        train_model(model, X, Y, epochs=epochs, lr=learning_rate)
        logging.info("Training completed.")
        save_model(model, save_path)
    
    # Run inference
    if args.inference:
        if args.input_id is None:
            sys.exit("Error: --input_id is required for inference.")
        run_inference(model, args.input_id)


if __name__ == "__main__":
    main()
