import torch
import torch.nn as nn

class HFMT8HighFidelityMultimodalTransformer(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Conv2d(**{"in_channels":3,"out_channels":1152,"kernel_size":14,"stride":14,"note":"SigLIP-style Patch Embedding for high-resolution input"}),
            nn.TransformerBlock(**{"hidden_size":1152,"num_heads":16,"mlp_ratio":4,"activation":"GELU","note":"SigLIP SO400M Vision Encoder Backbone"}),
            nn.Conv2d(**{"in_channels":1152,"out_channels":1152,"kernel_size":2,"stride":2,"note":"Adaptive Patch-Merging for 50% Visual Token Reduction"}),
            nn.Linear(**{"in_features":1152,"out_features":4096,"note":"Cross-Modal Projection Bridge to LLM Latent Space"}),
            nn.TransformerBlock(**{"hidden_size":4096,"num_attention_heads":32,"num_key_value_groups":8,"attention_type":"Grouped-Query Attention (GQA)","positional_encoding":"RoPE (Rotary)","note":"Llama-3 Decoder Block with 4-bit NF4 Quantization Support"}),
            nn.Linear(**{"in_features":4096,"out_features":14336,"activation":"SwiGLU","note":"Gated Linear Unit for Enhanced Representational Capacity"}),
            nn.RMSNorm(**{"normalized_shape":4096,"eps":0.00001,"note":"Pre-block Normalization for Numerical Stability"}),
            nn.Linear(**{"in_features":4096,"out_features":128256,"bias":false,"note":"Language Modeling Head (Uncensored Configuration)"})
        )

    def forward(self, x):
        return self.layers(x)