text: import copy
  import json
  import logging
  import multiprocessing
  import os
  from pathlib import Path
  from typing import Iterable, Dict, Union, List, Optional, Callable
  
  import numpy
  import torch
  from torch import nn, set_warn_always
  from transformers import AutoConfig
  from transformers.convert_graph_to_onnx import convert, quantize as quantize_model
  
  import haystack.modeling.conversion.transformers as conv
  from haystack.modeling.data_handler.processor import Processor
  from haystack.modeling.model.language_model import LanguageModel
  from haystack.modeling.model.prediction_head import PredictionHead
  from haystack.modeling.logger import MLFlowLogger as MlLogger
  
  
  logger = logging.getLogger(__name__)
  
  
  class BaseAdaptiveModel:
      """
      Base Class for implementing AdaptiveModel with frameworks like PyTorch and ONNX.
      """
  
      language_model: LanguageModel
      subclasses = {}  # type: Dict
  
      def __init_subclass__(cls, **kwargs):
          """
          This automatically keeps track of all available subclasses.
          Enables generic load() for all specific AdaptiveModel implementation.
          """
          super().__init_subclass__(**kwargs)
          cls.subclasses[cls.__name__] = cls
  
      def __init__(self, prediction_heads: Union[List[PredictionHead], nn.ModuleList]):
          self.prediction_heads = prediction_heads
  
      @classmethod
      def load(cls, **kwargs):
          """
          Load corresponding AdaptiveModel Class(AdaptiveModel/ONNXAdaptiveModel) based on the
          files in the load_dir.
  
          :param kwargs: Arguments to pass for loading the model.
          :return: Instance of a model.
          """
          if (Path(kwargs["load_dir"]) / "model.onnx").is_file():
              model = cls.subclasses["ONNXAdaptiveModel"].load(**kwargs)
          else:
              model = cls.subclasses["AdaptiveModel"].load(**kwargs)
          return model
  
      def logits_to_preds(self, logits: torch.Tensor, **kwargs):
          """
          Get predictions from all prediction heads.
  
          :param logits: Logits that can vary in shape and type, depending on task.
          :return: A list of all predictions from all prediction heads.
          """
          all_preds = []
          # collect preds from all heads
          for head, logits_for_head in zip(self.prediction_heads, logits):
              preds = head.logits_to_preds(logits=logits_for_head, **kwargs)
              all_preds.append(preds)
          return all_preds
  
      def formatted_preds(self, logits: torch.Tensor, **kwargs):
          """
          Format predictions for inference.
  
          :param logits: Model logits.
          :return: Predictions in the right format.
          """
          n_heads = len(self.prediction_heads)
  
          if n_heads == 0:
              # just return LM output (e.g. useful for extracting embeddings at inference time)
              preds_final = self.language_model.formatted_preds(logits=logits, **kwargs)
  
          elif n_heads == 1:
              preds_final = []
              # This try catch is to deal with the fact that sometimes we collect preds before passing it to
              # formatted_preds (see Inferencer._get_predictions_and_aggregate()) and sometimes we don't
              # (see Inferencer._get_predictions())
              try:
                  preds = kwargs["preds"]
                  temp = [y[0] for y in preds]
                  preds_flat = [item for sublist in temp for item in sublist]
                  kwargs["preds"] = preds_flat
              except KeyError:
                  kwargs["preds"] = None
              head = self.prediction_heads[0]
              logits_for_head = logits[0]
              preds = head.formatted_preds(logits=logits_for_head, **kwargs)
              # TODO This is very messy - we need better definition of what the output should look like
              if type(preds) == list:
                  preds_final += preds
              elif type(preds) == dict and "predictions" in preds:
                  preds_final.append(preds)
  
          return preds_final
  
      def connect_heads_with_processor(self, tasks: Dict, require_labels: bool = True):
          """
          Populates prediction head with information coming from tasks.
  
          :param tasks: A dictionary where the keys are the names of the tasks and
                        the values are the details of the task (e.g. label_list, metric,
                        tensor name).
          :param require_labels: If True, an error will be thrown when a task is
                                 not supplied with labels.
          :return: None
          """
          for head in self.prediction_heads:
              head.label_tensor_name = tasks[head.task_name]["label_tensor_name"]
              label_list = tasks[head.task_name]["label_list"]
              if not label_list and require_labels:
                  raise Exception(f"The task '{head.task_name}' is missing a valid set of labels")
              label_list = tasks[head.task_name]["label_list"]
              head.label_list = label_list
              head.metric = tasks[head.task_name]["metric"]
  
      @classmethod
      def _get_prediction_head_files(cls, load_dir: Union[str, Path], strict: bool = True):
          load_dir = Path(load_dir)
          files = os.listdir(load_dir)
          model_files = [load_dir / f for f in files if ".bin" in f and "prediction_head" in f]
          config_files = [load_dir / f for f in files if "config.json" in f and "prediction_head" in f]
          # sort them to get correct order in case of multiple prediction heads
          model_files.sort()
          config_files.sort()
  
          if strict:
              error_str = (
                  f"There is a mismatch in number of model files ({len(model_files)}) and config files ({len(config_files)})."
                  "This might be because the Language Model Prediction Head "
                  "does not currently support saving and loading"
              )
              assert len(model_files) == len(config_files), error_str
          logger.info(f"Found files for loading {len(model_files)} prediction heads")
  
          return model_files, config_files
  
  
  def loss_per_head_sum(loss_per_head: Iterable, global_step: Optional[int] = None, batch: Optional[Dict] = None):
      """
      Sums up the loss of each prediction head.
  
      :param loss_per_head: List of losses.
      """
      return sum(loss_per_head)
  
  
  class AdaptiveModel(nn.Module, BaseAdaptiveModel):
      """
      PyTorch implementation containing all the modelling needed for your NLP task. Combines a language
      model and a prediction head. Allows for gradient flow back to the language model component.
      """
  
      def __init__(
          self,
          language_model: LanguageModel,
          prediction_heads: List[PredictionHead],
          embeds_dropout_prob: float,
          lm_output_types: Union[str, List[str]],
          device: str,
          loss_aggregation_fn: Optional[Callable] = None,
      ):
          """
          :param language_model: Any model that turns token ids into vector representations.
          :param prediction_heads: A list of models that take embeddings and return logits for a given task.
          :param embeds_dropout_prob: The probability that a value in the embeddings returned by the
                                      language model will be zeroed.
          :param lm_output_types: How to extract the embeddings from the final layer of the language model. When set
                                  to "per_token", one embedding will be extracted per input token. If set to
                                  "per_sequence", a single embedding will be extracted to represent the full
                                  input sequence. Can either be a single string, or a list of strings,
                                  one for each prediction head.
          :param device: The device on which this model will operate. Either "cpu" or "cuda".
          :param loss_aggregation_fn: Function to aggregate the loss of multiple prediction heads.
                                      Input: loss_per_head (list of tensors), global_step (int), batch (dict)
                                      Output: aggregated loss (tensor)
                                      Default is a simple sum:
                                      `lambda loss_per_head, global_step=None, batch=None: sum(tensors)`
                                      However, you can pass more complex functions that depend on the
                                      current step (e.g. for round-robin style multitask learning) or the actual
                                      content of the batch (e.g. certain labels)
                                      Note: The loss at this stage is per sample, i.e one tensor of
                                      shape (batchsize) per prediction head.
          """
          super(AdaptiveModel, self).__init__()  # type: ignore
          self.device = device
          self.language_model = language_model.to(device)
          self.lm_output_dims = language_model.get_output_dims()
          self.prediction_heads = nn.ModuleList([ph.to(device) for ph in prediction_heads])
          self.fit_heads_to_lm()
          self.dropout = nn.Dropout(embeds_dropout_prob)
          self.lm_output_types = [lm_output_types] if isinstance(lm_output_types, str) else lm_output_types
          self.log_params()
          # default loss aggregation function is a simple sum (without using any of the optional params)
          if not loss_aggregation_fn:
              loss_aggregation_fn = loss_per_head_sum
          self.loss_aggregation_fn = loss_aggregation_fn
  
      def fit_heads_to_lm(self):
          """
          This iterates over each prediction head and ensures that its input
          dimensionality matches the output dimensionality of the language model.
          If it doesn't, it is resized so it does fit.
          """
          for ph in self.prediction_heads:
              ph.resize_input(self.lm_output_dims)
              ph.to(self.device)
  
      def bypass_ph(self):
          """
          Replaces methods in the prediction heads with dummy functions.
          Used for benchmarking where we want to isolate the LanguageModel run time
          from the PredictionHead run time.
          """
          # TODO convert inner functions into lambdas
  
          def fake_forward(x):
              """
              Slices lm vector outputs of shape (batch_size, max_seq_len, dims) --> (batch_size, max_seq_len, 2)
              """
              return x.narrow(2, 0, 2)
  
          def fake_logits_to_preds(logits, **kwargs):
              batch_size = logits.shape[0]
              return [None, None] * batch_size
  
          def fake_formatted_preds(**kwargs):
              return None
  
          for ph in self.prediction_heads:
              ph.forward = fake_forward
              ph.logits_to_preds = fake_logits_to_preds
              ph.formatted_preds = fake_formatted_preds
  
      def save(self, save_dir: Union[str, Path]):
          """
          Saves the language model and prediction heads. This will generate a config file
          and model weights for each.
  
          :param save_dir: Path to save the AdaptiveModel to.
          """
          os.makedirs(save_dir, exist_ok=True)
          self.language_model.save(save_dir)
          for i, ph in enumerate(self.prediction_heads):
              ph.save(save_dir, i)
              # Need to save config and pipeline
  
      @classmethod
      def load(  #  type: ignore
          cls,
          load_dir: Union[str, Path],  #  type: ignore
          device: str,  #  type: ignore
          strict: bool = True,  #  type: ignore
          lm_name: Optional[str] = None,  #  type: ignore
          processor: Optional[Processor] = None,  #  type: ignore
      ):
          """
          Loads an AdaptiveModel from a directory. The directory must contain:
  
          * language_model.bin
          * language_model_config.json
          * prediction_head_X.bin  multiple PH possible
          * prediction_head_X_config.json
          * processor_config.json config for transforming input
          * vocab.txt vocab file for language model, turning text to Wordpiece Tokens
  
          :param load_dir: Location where the AdaptiveModel is stored.
          :param device: To which device we want to sent the model, either cpu or cuda.
          :param lm_name: The name to assign to the loaded language model.
          :param strict: Whether to strictly enforce that the keys loaded from saved model match the ones in
                         the PredictionHead (see torch.nn.module.load_state_dict()).
          :param processor: Processor to populate prediction head with information coming from tasks.
          """
          # Language Model
          if lm_name:
              language_model = LanguageModel.load(load_dir, haystack_lm_name=lm_name)
          else:
              language_model = LanguageModel.load(load_dir)
  
          # Prediction heads
          _, ph_config_files = cls._get_prediction_head_files(load_dir)
          prediction_heads = []
          ph_output_type = []
          for config_file in ph_config_files:
              head = PredictionHead.load(config_file, strict=strict)
              prediction_heads.append(head)
              ph_output_type.append(head.ph_output_type)
  
          model = cls(language_model, prediction_heads, 0.1, ph_output_type, device)
          if processor:
              model.connect_heads_with_processor(processor.tasks)
  
          return model
  
      def logits_to_loss_per_head(self, logits: torch.Tensor, **kwargs):
          """
          Collect losses from each prediction head.
  
          :param logits: Logits, can vary in shape and type, depending on task.
          :return: The per sample per prediciton head loss whose first two dimensions
                   have length n_pred_heads, batch_size.
          """
          all_losses = []
          for head, logits_for_one_head in zip(self.prediction_heads, logits):
              # check if PredictionHead connected to Processor
              assert hasattr(head, "label_tensor_name"), (
                  f"Label_tensor_names are missing inside the {head.task_name} Prediction Head. Did you connect the model"
                  " with the processor through either 'model.connect_heads_with_processor(processor.tasks)'"
                  " or by passing the processor to the Adaptive Model?"
              )
              all_losses.append(head.logits_to_loss(logits=logits_for_one_head, **kwargs))
          return all_losses
  
      def logits_to_loss(self, logits: torch.Tensor, global_step: Optional[int] = None, **kwargs):
          """
          Get losses from all prediction heads & reduce to single loss *per sample*.
  
          :param logits: Logits, can vary in shape and type, depending on task.
          :param global_step: Number of current training step.
          :param kwargs: Placeholder for passing generic parameters.
                         Note: Contains the batch (as dict of tensors), when called from Trainer.train().
          :return: torch.tensor that is the per sample loss (len: batch_size)
          """
          all_losses = self.logits_to_loss_per_head(logits, **kwargs)
          # This aggregates the loss per sample across multiple prediction heads
          # Default is sum(), but you can configure any fn that takes [Tensor, Tensor ...] and returns [Tensor]
          loss = self.loss_aggregation_fn(all_losses, global_step=global_step, batch=kwargs)
          return loss
  
      def prepare_labels(self, **kwargs):
          """
          Label conversion to original label space, per prediction head.
  
          :param label_maps: dictionary for mapping ids to label strings
          :type label_maps: dict[int:str]
          :return: labels in the right format
          """
          all_labels = []
          # for head, label_map_one_head in zip(self.prediction_heads):
          #     labels = head.prepare_labels(label_map=label_map_one_head, **kwargs)
          #     all_labels.append(labels)
          for head in self.prediction_heads:
              labels = head.prepare_labels(**kwargs)
              all_labels.append(labels)
          return all_labels
  
      def forward(self, output_hidden_states: bool = False, output_attentions: bool = False, **kwargs):
          """
          Push data through the whole model and returns logits. The data will
          propagate through the language model and each of the attached prediction heads.
  
          :param kwargs: Holds all arguments that need to be passed to the language model
                         and prediction head(s).
          :param output_hidden_states: Whether to output hidden states
          :param output_attentions: Whether to output attentions
          :return: All logits as torch.tensor or multiple tensors.
          """
          # Run forward pass of language model
          output_tuple = self.language_model.forward(
              **kwargs, output_hidden_states=output_hidden_states, output_attentions=output_attentions
          )
          if output_hidden_states:
              if output_attentions:
                  sequence_output, pooled_output, hidden_states, attentions = output_tuple
              else:
                  sequence_output, pooled_output, hidden_states = output_tuple
          else:
              if output_attentions:
                  sequence_output, pooled_output, attentions = output_tuple
              else:
                  sequence_output, pooled_output = output_tuple
          # Run forward pass of (multiple) prediction heads using the output from above
          all_logits = []
          if len(self.prediction_heads) > 0:
              for head, lm_out in zip(self.prediction_heads, self.lm_output_types):
                  # Choose relevant vectors from LM as output and perform dropout
                  if lm_out == "per_token":
                      output = self.dropout(sequence_output)
                  elif lm_out == "per_sequence" or lm_out == "per_sequence_continuous":
                      output = self.dropout(pooled_output)
                  elif (
                      lm_out == "per_token_squad"
                  ):  # we need a per_token_squad because of variable metric computation later on...
                      output = self.dropout(sequence_output)
                  else:
                      raise ValueError("Unknown extraction strategy from language model: {}".format(lm_out))
  
                  # Do the actual forward pass of a single head
                  all_logits.append(head(output))
          else:
              # just return LM output (e.g. useful for extracting embeddings at inference time)
              all_logits.append((sequence_output, pooled_output))
  
          if output_hidden_states:
              if output_attentions:
                  return all_logits, hidden_states, attentions
              else:
                  return all_logits, hidden_states
          elif output_attentions:
              return all_logits, attentions
          return all_logits
  
      def forward_lm(self, **kwargs):
          """
          Forward pass for the language model.
  
          :return: Tuple containing list of embeddings for each token and
                   embedding for whole sequence.
          """
          # Check if we have to extract from a special layer of the LM (default = last layer)
          try:
              extraction_layer = self.language_model.extraction_layer
          except:
              extraction_layer = -1
  
          # Run forward pass of language model
          if extraction_layer == -1:
              sequence_output, pooled_output = self.language_model(
                  **kwargs, return_dict=False, output_all_encoded_layers=False
              )
          else:
              # get output from an earlier layer
              self.language_model.enable_hidden_states_output()
              sequence_output, pooled_output, all_hidden_states = self.language_model(**kwargs, return_dict=False)
              sequence_output = all_hidden_states[extraction_layer]
              pooled_output = None  # not available in earlier layers
              self.language_model.disable_hidden_states_output()
          return sequence_output, pooled_output
  
      def log_params(self):
          """
          Logs parameteres to generic logger MlLogger
          """
          params = {
              "lm_type": self.language_model.__class__.__name__,
              "lm_name": self.language_model.name,
              "prediction_heads": ",".join([head.__class__.__name__ for head in self.prediction_heads]),
              "lm_output_types": ",".join(self.lm_output_types),
          }
          try:
              MlLogger.log_params(params)
          except Exception as e:
              logger.warning(f"ML logging didn't work: {e}")
  
      def verify_vocab_size(self, vocab_size: int):
          """
          Verifies that the model fits to the tokenizer vocabulary.
          They could diverge in case of custom vocabulary added via tokenizer.add_tokens()
          """
          model_vocab_len = self.language_model.model.resize_token_embeddings(new_num_tokens=None).num_embeddings
  
          msg = (
              f"Vocab size of tokenizer {vocab_size} doesn't match with model {model_vocab_len}. "
              "If you added a custom vocabulary to the tokenizer, "
              "make sure to supply 'n_added_tokens' to LanguageModel.load() and BertStyleLM.load()"
          )
          assert vocab_size == model_vocab_len, msg
  
          for head in self.prediction_heads:
              if head.model_type == "language_modelling":
                  ph_decoder_len = head.decoder.weight.shape[0]
                  assert vocab_size == ph_decoder_len, msg
  
      def get_language(self):
          return self.language_model.language
  
      def convert_to_transformers(self):
          """
          Convert an adaptive model to huggingface's transformers format. Returns a list containing one model for each
          prediction head.
  
          :return: List of huggingface transformers models.
          """
          return conv.Converter.convert_to_transformers(self)
  
      @classmethod
      def convert_from_transformers(
          cls,
          model_name_or_path: Union[str, Path],
          device: str,
          revision: Optional[str] = None,
          task_type: Optional[str] = None,
          processor: Optional[Processor] = None,
          use_auth_token: Optional[Union[bool, str]] = None,
          **kwargs,
      ):
          """
          Load a (downstream) model from huggingface's transformers format. Use cases:
           - continue training in Haystack (e.g. take a squad QA model and fine-tune on your own data)
           - compare models without switching frameworks
           - use model directly for inference
  
          :param model_name_or_path: local path of a saved model or name of a public one.
                                                Exemplary public names:
                                                - distilbert-base-uncased-distilled-squad
                                                - deepset/bert-large-uncased-whole-word-masking-squad2
  
                                                See https://huggingface.co/models for full list
          :param revision: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
          :param device: "cpu" or "cuda"
          :param task_type: One of :
                            - 'question_answering'
                            More tasks coming soon ...
          :param processor: Processor to populate prediction head with information coming from tasks.
          :type processor: Processor
          :return: AdaptiveModel
          """
          return conv.Converter.convert_from_transformers(
              model_name_or_path,
              revision=revision,
              device=device,
              task_type=task_type,
              processor=processor,
              use_auth_token=use_auth_token,
              **kwargs,
          )
  
      @classmethod
      def convert_to_onnx(
          cls,
          model_name: str,
          output_path: Path,
          task_type: str,
          convert_to_float16: bool = False,
          quantize: bool = False,
          opset_version: int = 11,
      ):
          """
          Convert a PyTorch model from transformers hub to an ONNX Model.
  
          :param model_name: Transformers model name.
          :param output_path: Output Path to write the converted model to.
          :param task_type: Type of task for the model. Available options: "question_answering"
          :param convert_to_float16: By default, the model uses float32 precision. With half precision of float16, inference
                                     should be faster on Nvidia GPUs with Tensor core like T4 or V100. On older GPUs, float32
                                     might be more performant.
          :param quantize: Convert floating point number to integers
          :param opset_version: ONNX opset version.
          :return: None.
          """
          language_model_class = LanguageModel.get_language_model_class(model_name)
          if language_model_class not in ["Bert", "Roberta", "XLMRoberta"]:
              raise Exception("The current ONNX conversion only support 'BERT', 'RoBERTa', and 'XLMRoberta' models.")
  
          task_type_to_pipeline_map = {
              "question_answering": "question-answering",
          }
  
          convert(
              pipeline_name=task_type_to_pipeline_map[task_type],
              framework="pt",
              model=model_name,
              output=output_path / "model.onnx",
              opset=opset_version,
              use_external_format=True if language_model_class == "XLMRoberta" else False,
          )
  
          # save processor & model config files that are needed when loading the model with the Haystack.basics Inferencer
          processor = Processor.convert_from_transformers(
              tokenizer_name_or_path=model_name, task_type=task_type, max_seq_len=256, doc_stride=128, use_fast=True
          )
          processor.save(output_path)
          model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type=task_type)
          model.save(output_path)
          os.remove(output_path / "language_model.bin")  # remove the actual PyTorch model(only configs are required)
  
          onnx_model_config = {
              "task_type": task_type,
              "onnx_opset_version": opset_version,
              "language_model_class": language_model_class,
              "language": model.language_model.language,
          }
          with open(output_path / "onnx_model_config.json", "w") as f:
              json.dump(onnx_model_config, f)
  
          if convert_to_float16:
              from onnxruntime_tools import optimizer
  
              config = AutoConfig.from_pretrained(model_name)
              optimized_model = optimizer.optimize_model(
                  input=str(output_path / "model.onnx"),
                  model_type="bert",
                  num_heads=config.num_hidden_layers,
                  hidden_size=config.hidden_size,
              )
              optimized_model.convert_model_float32_to_float16()
              optimized_model.save_model_to_file("model.onnx")
  
          if quantize:
              quantize_model(output_path / "model.onnx")
  
  
  class ONNXAdaptiveModel(BaseAdaptiveModel):
      """
      Implementation of ONNX Runtime for Inference of ONNX Models.
  
      Existing PyTorch based Haystack.basics AdaptiveModel can be converted to ONNX format using AdaptiveModel.convert_to_onnx().
      The conversion is currently only implemented for Question Answering Models.
  
      For inference, this class is compatible with the Haystack.basics Inferencer.
      """
  
      # TODO validate usefulness
      def __init__(
          self,
          onnx_session,  # TODO
          language_model_class: str,
          language: str,
          prediction_heads: List[PredictionHead],
          device: str,
      ):
          """
          :param onnx_session: ? # TODO
          :param language_model_class: Class of LanguageModel
          :param langauge: Language the model is trained for.
          :param prediction_heads: A list of models that take embeddings and return logits for a given task.
          :param device: The device on which this model will operate. Either "cpu" or "cuda".
          """
          import onnxruntime
  
          if str(device) == "cuda" and onnxruntime.get_device() != "GPU":
              raise Exception(
                  f"Device {device} not available for Inference. For CPU, run pip install onnxruntime and"
                  f"for GPU run pip install onnxruntime-gpu"
              )
          self.onnx_session = onnx_session
          self.language_model_class = language_model_class
          self.language = language
          self.prediction_heads = prediction_heads
          self.device = device
  
      @classmethod
      def load(cls, load_dir: Union[str, Path], device: str, **kwargs):  # type: ignore
          """
          Loads an ONNXAdaptiveModel from a directory.
  
          :param load_dir: Location where the ONNXAdaptiveModel is stored.
          :param device: The device on which this model will operate. Either "cpu" or "cuda".
          """
          load_dir = Path(load_dir)
          import onnxruntime
  
          sess_options = onnxruntime.SessionOptions()
          # Set graph optimization level to ORT_ENABLE_EXTENDED to enable bert optimization.
          sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
          # Use OpenMP optimizations. Only useful for CPU, has little impact for GPUs.
          sess_options.intra_op_num_threads = multiprocessing.cpu_count()
          onnx_session = onnxruntime.InferenceSession(str(load_dir / "model.onnx"), sess_options)
  
          # Prediction heads
          _, ph_config_files = cls._get_prediction_head_files(load_dir, strict=False)
          prediction_heads = []
          ph_output_type = []
          for config_file in ph_config_files:
              # ONNX Model doesn't need have a separate neural network for PredictionHead. It only uses the
              # instance methods of PredictionHead class, so, we load with the load_weights param as False.
              head = PredictionHead.load(config_file, load_weights=False)
              prediction_heads.append(head)
              ph_output_type.append(head.ph_output_type)
  
          with open(load_dir / "onnx_model_config.json") as f:
              model_config = json.load(f)
              language_model_class = model_config["language_model_class"]
              language = model_config["language"]
  
          return cls(onnx_session, language_model_class, language, prediction_heads, device)
  
      def forward(self, **kwargs):
          """
          Perform forward pass on the model and return the logits.
  
          :param kwargs: All arguments that need to be passed on to the model.
          :return: All logits as torch.tensor or multiple tensors.
          """
          with torch.no_grad():
              if self.language_model_class == "Bert":
                  input_to_onnx = {
                      "input_ids": numpy.ascontiguousarray(kwargs["input_ids"].cpu().numpy()),
                      "attention_mask": numpy.ascontiguousarray(kwargs["padding_mask"].cpu().numpy()),
                      "token_type_ids": numpy.ascontiguousarray(kwargs["segment_ids"].cpu().numpy()),
                  }
              elif self.language_model_class in ["Roberta", "XLMRoberta"]:
                  input_to_onnx = {
                      "input_ids": numpy.ascontiguousarray(kwargs["input_ids"].cpu().numpy()),
                      "attention_mask": numpy.ascontiguousarray(kwargs["padding_mask"].cpu().numpy()),
                  }
              res = self.onnx_session.run(None, input_to_onnx)
              res = numpy.stack(res).transpose(1, 2, 0)
              logits = [torch.Tensor(res).to(self.device)]
  
          return logits
  
      def eval(self):
          """
          Stub to make ONNXAdaptiveModel compatible with the PyTorch AdaptiveModel.
          """
          return True
  
      def get_language(self):
          """
          Get the language(s) the model was trained for.
          :return: str
          """
          return self.language
  
  
  class ONNXWrapper(AdaptiveModel):
      """
      Wrapper Class for converting PyTorch models to ONNX.
  
      As of torch v1.4.0, torch.onnx.export only support passing positional arguments
      to the forward pass of the model. However, the AdaptiveModel's forward takes keyword arguments.
      This class circumvents the issue by converting positional arguments to keyword arguments.
      """
  
      @classmethod
      def load_from_adaptive_model(cls, adaptive_model: AdaptiveModel):
          model = copy.deepcopy(adaptive_model)
          model.__class__ = ONNXWrapper
          return model
  
      def forward(self, *batch):
          return super().forward(input_ids=batch[0], padding_mask=batch[1], segment_ids=batch[2])
  