src/transformers/onnx/config.py/OnnxConfig/generate_dummy_inputs
class OnnxConfig:    def generate_dummy_inputs(
        self,
        tokenizer: PreTrainedTokenizer,
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        """
        Generate inputs to provide to the ONNX exporter for the specific framework

        Args:
            tokenizer: The tokenizer associated with this model configuration
            batch_size: The batch size (int) to export the model for (-1 means dynamic axis)
            seq_length: The sequence length (int) to export the model for (-1 means dynamic axis)
            is_pair: Indicate if the input is a pair (sentence 1, sentence 2)
            framework: The framework (optional) the tokenizer will generate tensor for

        Returns:
            Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
        """

        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        batch_size = compute_effective_axis_dimension(
            batch_size, fixed_dimension=OnnxConfig.DEFAULT_FIXED_BATCH, num_token_to_add=0
        )

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
        seq_length = compute_effective_axis_dimension(
            seq_length, fixed_dimension=OnnxConfig.DEFAULT_FIXED_SEQUENCE, num_token_to_add=token_to_add
        )

        # Generate dummy inputs according to compute batch and sequence
        dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
        return dict(tokenizer(dummy_input, return_tensors=framework))