// File: Wav2Vec2ForSequenceClassificationSharp.cs
using TorchSharp;
using TorchSharp.Modules;
using static TorchSharp.torch;
using static TorchSharp.torch.nn;
public class Wav2Vec2ForSequenceClassificationSharp : Module<Tensor, Tensor>
{
 // Direct children to match the Python model's structure.
 private readonly Wav2Vec2Model wav2vec2;
 private readonly Linear projector;
 private readonly Linear classifier;
 public Wav2Vec2ForSequenceClassificationSharp(
 torchaudio.models.FeatureExtractorNormMode extractor_mode,
 long[][] extractor_conv_layer_config, bool extractor_conv_bias,
 int encoder_embed_dim, double encoder_projection_dropout,
 int encoder_pos_conv_kernel, int encoder_pos_conv_groups,
 int encoder_num_layers, int encoder_num_heads,
 double encoder_attention_dropout, int encoder_ff_interm_features,
 double encoder_ff_interm_dropout, double encoder_dropout,
 bool encoder_layer_norm_first, double encoder_layer_drop,
 int classifier_proj_size, int num_labels
 ) : base(nameof(Wav2Vec2ForSequenceClassificationSharp))
 {
 // The names of these member variables ("wav2vec2", "projector", "classifier")
 // must exactly match the prefixes in the state_dict.
 this.wav2vec2 = torchaudio.models.wav2vec2_model(
 extractor_mode, extractor_conv_layer_config, extractor_conv_bias,
 encoder_embed_dim, encoder_projection_dropout, encoder_pos_conv_kernel,
 encoder_pos_conv_groups, encoder_num_layers, encoder_num_heads,
 encoder_attention_dropout, encoder_ff_interm_features, encoder_ff_interm_dropout,
 encoder_dropout, encoder_layer_norm_first, encoder_layer_drop,
 aux_num_out: null
 );
 
 this.projector = Linear(encoder_embed_dim, classifier_proj_size);
 this.classifier = Linear(classifier_proj_size, num_labels);
 RegisterComponents();
 }
 public override Tensor forward(Tensor input)
 {
 var (hidden_states, _) = this.wav2vec2.forward(input);
 var projected_states = this.projector.forward(hidden_states);
 var pooled_output = projected_states.mean(new long[] { 1 });
 var logits = this.classifier.forward(pooled_output);
 return logits;
 }
}

// File: Program.cs
using TorchSharp;
using static TorchSharp.torch;
public class Program
{
 static void Main(string[] args)
 {
 Console.WriteLine("Attempting to load Wav2Vec2ForSequenceClassification model...");
 // Model Configuration from config.json
 var extractor_mode = torchaudio.models.FeatureExtractorNormMode.layer_norm;
 var extractor_conv_layer_config = new long[][] {
 new long[] { 512, 10, 5 }, new long[] { 512, 3, 2 }, new long[] { 512, 3, 2 },
 new long[] { 512, 3, 2 }, new long[] { 512, 3, 2 }, new long[] { 512, 2, 2 },
 new long[] { 512, 2, 2 }
 };
 var extractor_conv_bias = true;
 var encoder_embed_dim = 1024;
 var encoder_projection_dropout = 0.1;
 var encoder_pos_conv_kernel = 128;
 var encoder_pos_conv_groups = 16;
 var encoder_num_layers = 24;
 var encoder_num_heads = 16;
 var encoder_attention_dropout = 0.1;
 var encoder_ff_interm_features = 4096;
 var encoder_ff_interm_dropout = 0.0;
 var encoder_dropout = 0.1;
 var encoder_layer_norm_first = true;
 var encoder_layer_drop = 0.1;
 var classifier_proj_size = 256;
 var num_labels = 2;
 try
 {
 var model = new Wav2Vec2ForSequenceClassificationSharp(
 extractor_mode, extractor_conv_layer_config, extractor_conv_bias,
 encoder_embed_dim, encoder_projection_dropout, encoder_pos_conv_kernel,
 encoder_pos_conv_groups, encoder_num_layers, encoder_num_heads,
 encoder_attention_dropout, encoder_ff_interm_features,
 encoder_ff_interm_dropout, encoder_dropout,
 encoder_layer_norm_first, encoder_layer_drop,
 classifier_proj_size, num_labels
 );
 var modelPath = "path/to/your/converted_model.bin";
 
 // This is where the exception is thrown.
 model.load(modelPath, strict: true); 
 Console.WriteLine("Model loaded successfully!");
 }
 catch (Exception ex)
 {
 Console.WriteLine("\nERROR: Failed to load model.");
 Console.WriteLine(ex.ToString());
 }
 }
}