def forward(
        self,
        input_values: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, UniSpeechForPreTrainingOutput]:
        r"""
        mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
            masked extracted features in *config.proj_codevector_dim* space.
        sampled_negative_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_negatives)`, *optional*):
            Indices indicating which quantized target vectors are used as negative sampled vectors in contrastive loss.
            Required input for pre-training.

        Returns:

        Example:

        ```python
        >>> import torch
        >>> from transformers import AutoFeatureExtractor, UniSpeechForPreTraining

        >>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/unispeech-large-1500h-cv")
        >>> model = UniSpeechForPreTraining.from_pretrained("microsoft/unispeech-large-1500h-cv")
        >>> # TODO: Add full pretraining example
        ```"""

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.unispeech(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        transformer_features = outputs[0]

        # quantize all (unmasked) extracted features and project to final vq dim
        extract_features = self.dropout_features(outputs[1])
        quantized_features, codevector_perplexity = self.quantizer(extract_features)

        # project quantized features twice
        quantized_features = self.project_q(quantized_features)
        quantized_features = self.project_hid(quantized_features)

        prob_replace_matrix = torch.empty(transformer_features.size(0), transformer_features.size(1)).fill_(
            self.config.replace_prob
        )
        prob_replace_matrix = prob_replace_matrix.transpose(0, 1)
        sampled_replace_matrix = torch.bernoulli(prob_replace_matrix).bool().to(transformer_features.device)
        sampled_replace_matrix = sampled_replace_matrix.transpose(0, 1)
        sampled_replace_matrix = sampled_replace_matrix.unsqueeze(-1)
        logits = transformer_features.masked_fill(sampled_replace_matrix, 0.0) + (
            quantized_features.masked_fill(~sampled_replace_matrix, 0.0)
        )

        # project to ctc units
        logits = self.dropout(logits)
        logits = self.ctc_proj(logits)

        # TODO(PVP) - add negative sampling & loss computation
        loss = None
        if not return_dict:
            if loss is not None:
                return (loss, transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
            return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:]

        return UniSpeechForPreTrainingOutput(
            loss=loss,
            projected_states=transformer_features,
            projected_quantized_states=quantized_features,
            codevector_perplexity=codevector_perplexity,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

        mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
            masked extracted features in *config.proj_codevector_dim* space.
        sampled_negative_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_negatives)`, *optional*):
            Indices indicating which quantized target vectors are used as negative sampled vectors in contrastive loss.
            Required input for pre-training.

        Returns:

        Example:

        ```python
        >>> import torch
        >>> from transformers import AutoFeatureExtractor, UniSpeechForPreTraining

        >>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/unispeech-large-1500h-cv")
        >>> model = UniSpeechForPreTraining.from_pretrained("microsoft/unispeech-large-1500h-cv")
        >>> # TODO: Add full pretraining example
        ```
dpkd/transformers/src/transformers/models/unispeech/modeling_unispeech.py