# Import necessary libraries
import torch
import torch.nn as nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer
import math
from typing import cast

# New import for TDA and quantum feature encoding
from gudhi.representations.vector_methods import Landscape

class QuantumTransformer(nn.Module):
    def __init__(self, feature_size, num_layers, num_heads, dropout_rate):
        super(QuantumTransformer, self).__init__()
        # Configuration suitable for quantum data
        self.model_type = 'Transformer'
        self.pos_encoder = PositionalEncoding(feature_size, dropout_rate)
        encoder_layers = TransformerEncoderLayer(d_model=feature_size, nhead=num_heads, dropout=dropout_rate)
        self.transformer_encoder = TransformerEncoder(encoder_layers, num_layers)
        self.encoder = nn.Linear(feature_size, feature_size)
        self.decoder = nn.Linear(feature_size, feature_size)
        self.init_weights()
        # Set default training epochs and a default quantum feature encoder to satisfy linter
        self.num_epochs: int = 10
        self.quantum_feature_encoder = nn.Identity()

    def init_weights(self):
        initrange = 0.1
        self.encoder.weight.data.uniform_(-initrange, initrange)
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, src):
        src = self.encoder(src)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src)
        output = self.decoder(output)
        return output

    def preprocess_data(self, data):
        # Implement TDA and other preprocessing steps
        # Example: Convert data to persistence landscapes for TDA features
        landscapes = Landscape(resolution=100)
        tda_features = landscapes.fit_transform(data)
        return tda_features

    def encode_quantum_features(self, classical_data):
        # Placeholder for quantum feature encoding
        # Example: Use kernel methods or variational circuits
        quantum_data = self.quantum_feature_encoder(classical_data)
        return quantum_data

    def train(self, train_data, validation_data):
        # Enhanced training loop with quantum-inspired validation
        for epoch in range(self.num_epochs):
            # Training step
            # ...
            # Validation step with quantum metrics
            # ...
            pass

    def deploy(self):
        # Prepare the model for deployment
        # Example: Containerization, microservices setup, monitoring
        pass

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        position = torch.arange(max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model))
        pe = torch.zeros(max_len, 1, d_model)
        pe[:, 0, 0::2] = torch.sin(position * div_term)
        pe[:, 0, 1::2] = torch.cos(position * div_term)
        self.register_buffer('pe', pe)

    def forward(self, x):
        # Explicitly cast buffer to Tensor for type narrowing
        pe = cast(torch.Tensor, self.pe)
        x = x + pe[:x.size(0)]
        return self.dropout(x)

# Example usage:
# feature_size = 512  # Size of the feature vector
# num_layers = 6     # Number of transformer layers
# num_heads = 8      # Number of heads in the multi-head attention mechanisms
# dropout_rate = 0.1 # Dropout rate

# model = QuantumTransformer(feature_size, num_layers, num_heads, dropout_rate)
# src = torch.rand(10, 32, feature_size)  # Example input (sequence length, batch size, feature size)
# output = model(src)

# Additional functions for model design can be added here