





'''simple docstring'''






import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				TapasConfig.from_json_file(lowerCAmelCase    )
      # set absolute/relative position embeddings parameter
      _lowerCAmelCase					=				reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            _lowerCAmelCase					=				TapasForQuestionAnswering(config=lowerCAmelCase    )
      elif task == "WTQ":
            # run_task_main.py hparams
            _lowerCAmelCase					=				4
            _lowerCAmelCase					=				True
            # hparam_utils.py hparams
            _lowerCAmelCase					=				0.664_694
            _lowerCAmelCase					=				0.207_951
            _lowerCAmelCase					=				0.121_194
            _lowerCAmelCase					=				True
            _lowerCAmelCase					=				True
            _lowerCAmelCase					=				False
            _lowerCAmelCase					=				0.0_352_513

            _lowerCAmelCase					=				TapasForQuestionAnswering(config=lowerCAmelCase    )
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            _lowerCAmelCase					=				4
            _lowerCAmelCase					=				False
            # hparam_utils.py hparams
            _lowerCAmelCase					=				36.4_519
            _lowerCAmelCase					=				0.903_421
            _lowerCAmelCase					=				222.088
            _lowerCAmelCase					=				True
            _lowerCAmelCase					=				True
            _lowerCAmelCase					=				True
            _lowerCAmelCase					=				0.763_141

            _lowerCAmelCase					=				TapasForQuestionAnswering(config=lowerCAmelCase    )
      elif task == "TABFACT":
            _lowerCAmelCase					=				TapasForSequenceClassification(config=lowerCAmelCase    )
      elif task == "MLM":
            _lowerCAmelCase					=				TapasForMaskedLM(config=lowerCAmelCase    )
      elif task == "INTERMEDIATE_PRETRAINING":
            _lowerCAmelCase					=				TapasModel(config=lowerCAmelCase    )
      else:
            raise ValueError(f"Task {task} not supported."    )

      print(f"Building PyTorch model from configuration: {config}"    )
      # Load weights from tf checkpoint
      load_tf_weights_in_tapas(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      # Save pytorch-model (weights and configuration)
      print(f"Save PyTorch model to {pytorch_dump_path}"    )
      model.save_pretrained(lowerCAmelCase    )

      # Save tokenizer files
      print(f"Save tokenizer files to {pytorch_dump_path}"    )
      _lowerCAmelCase					=				TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""				,      model_max_length=5_12    )
      tokenizer.save_pretrained(lowerCAmelCase    )

      print("""Used relative position embeddings:"""				,      model.config.reset_position_index_per_cell    )


if __name__ == "__main__":
      A__							:  List[str]    =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.'''
      )
      A__							:  Tuple    =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,
      )













'''simple docstring'''






class 					UpperCAmelCase	:




      def __init__(     self     :					List[str]     ,     __snake_case     :					str    )							-> Union[str, Any]:
            _lowerCAmelCase					=				val
            _lowerCAmelCase					=				None
            _lowerCAmelCase					=				None




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					int    )							-> Optional[int]:
            if self.val:
                  if val < self.val:
                        if self.left is None:
                              _lowerCAmelCase					=				Node(__snake_case    )
                        else:
                              self.left.insert(__snake_case    )
                  elif val > self.val:
                        if self.right is None:
                              _lowerCAmelCase					=				Node(__snake_case    )
                        else:
                              self.right.insert(__snake_case    )
            else:
                  _lowerCAmelCase					=				val




def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      if root:
            inorder(root.left				,      lowerCAmelCase    )
            res.append(root.val    )
            inorder(root.right				,      lowerCAmelCase    )
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if len(lowerCAmelCase    ) == 0:
            return arr
      _lowerCAmelCase					=				Node(arr[0]    )
      for i in range(1				,      len(lowerCAmelCase    )    ):
            root.insert(arr[i]    )
      # Traverse BST in order.
      _lowerCAmelCase					=				[]
      inorder(lowerCAmelCase				,      lowerCAmelCase    )
      return res


if __name__ == "__main__":
      print(tree_sort([10, 1, 3, 2, 9, 14, 13]))






