code: # coding=utf-8
  # Copyright 2020 The HuggingFace Inc. team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """Convert TAPAS checkpoint."""
  
  
  import argparse
  
  from transformers.models.tapas.modeling_tapas import (
      TapasConfig,
      TapasForMaskedLM,
      TapasForQuestionAnswering,
      TapasForSequenceClassification,
      TapasModel,
      load_tf_weights_in_tapas,
  )
  from transformers.models.tapas.tokenization_tapas import TapasTokenizer
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  def convert_tf_checkpoint_to_pytorch(
      task, reset_position_index_per_cell, tf_checkpoint_path, tapas_config_file, pytorch_dump_path
  ):
      # Initialise PyTorch model.
      # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
      # TapasConfig to False.
  
      # initialize configuration from json file
      config = TapasConfig.from_json_file(tapas_config_file)
      # set absolute/relative position embeddings parameter
      config.reset_position_index_per_cell = reset_position_index_per_cell
  
      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
          model = TapasForQuestionAnswering(config=config)
      elif task == "WTQ":
          # run_task_main.py hparams
          config.num_aggregation_labels = 4
          config.use_answer_as_supervision = True
          # hparam_utils.py hparams
          config.answer_loss_cutoff = 0.664694
          config.cell_selection_preference = 0.207951
          config.huber_loss_delta = 0.121194
          config.init_cell_selection_weights_to_zero = True
          config.select_one_column = True
          config.allow_empty_column_selection = False
          config.temperature = 0.0352513
  
          model = TapasForQuestionAnswering(config=config)
      elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          config.num_aggregation_labels = 4
          config.use_answer_as_supervision = False
          # hparam_utils.py hparams
          config.answer_loss_cutoff = 36.4519
          config.cell_selection_preference = 0.903421
          config.huber_loss_delta = 222.088
          config.init_cell_selection_weights_to_zero = True
          config.select_one_column = True
          config.allow_empty_column_selection = True
          config.temperature = 0.763141
  
          model = TapasForQuestionAnswering(config=config)
      elif task == "TABFACT":
          model = TapasForSequenceClassification(config=config)
      elif task == "MLM":
          model = TapasForMaskedLM(config=config)
      elif task == "INTERMEDIATE_PRETRAINING":
          model = TapasModel(config=config)
  
      print("Building PyTorch model from configuration: {}".format(str(config)))
  
      # Load weights from tf checkpoint
      load_tf_weights_in_tapas(model, config, tf_checkpoint_path)
  
      # Save pytorch-model (weights and configuration)
      print("Save PyTorch model to {}".format(pytorch_dump_path))
      model.save_pretrained(pytorch_dump_path[:-17])
  
      # Save tokenizer files
      dir_name = r"C:\Users\niels.rogge\Documents\Python projecten\tensorflow\Tensorflow models\SQA\Base\tapas_sqa_inter_masklm_base_reset"
      tokenizer = TapasTokenizer(vocab_file=dir_name + r"\vocab.txt", model_max_length=512)
  
      print("Save tokenizer files to {}".format(pytorch_dump_path))
      tokenizer.save_pretrained(pytorch_dump_path[:-17])
  
      print("Used relative position embeddings:", model.config.reset_position_index_per_cell)
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
      )
      parser.add_argument(
          "--reset_position_index_per_cell",
          default=False,
          action="store_true",
          help="Whether to use relative position embeddings or not. Defaults to True.",
      )
      parser.add_argument(
          "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--tapas_config_file",
          default=None,
          type=str,
          required=True,
          help="The config json file corresponding to the pre-trained TAPAS model. \n"
          "This specifies the model architecture.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      args = parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.task,
          args.reset_position_index_per_cell,
          args.tf_checkpoint_path,
          args.tapas_config_file,
          args.pytorch_dump_path,
      )
  