

"""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 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    TapasConfig.from_json_file(_lowerCamelCase   )
    # set absolute/relative position embeddings parameter
    _lowerCAmelCase       :   Optional[int]  =    reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        _lowerCAmelCase       :   Optional[Any]  =    TapasForQuestionAnswering(config=_lowerCamelCase   )
    elif task == "WTQ":
        # run_task_main.py hparams
        _lowerCAmelCase       :   Any  =    4
        _lowerCAmelCase       :   Optional[int]  =    True
        # hparam_utils.py hparams
        _lowerCAmelCase       :   Any  =    0.664694
        _lowerCAmelCase       :   str  =    0.207951
        _lowerCAmelCase       :   List[Any]  =    0.121194
        _lowerCAmelCase       :   Optional[int]  =    True
        _lowerCAmelCase       :   Optional[int]  =    True
        _lowerCAmelCase       :   Optional[int]  =    False
        _lowerCAmelCase       :   str  =    0.0352513

        _lowerCAmelCase       :   int  =    TapasForQuestionAnswering(config=_lowerCamelCase   )
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        _lowerCAmelCase       :   Tuple  =    4
        _lowerCAmelCase       :   Any  =    False
        # hparam_utils.py hparams
        _lowerCAmelCase       :   List[Any]  =    36.4519
        _lowerCAmelCase       :   List[Any]  =    0.903421
        _lowerCAmelCase       :   int  =    222.088
        _lowerCAmelCase       :   Dict  =    True
        _lowerCAmelCase       :   Tuple  =    True
        _lowerCAmelCase       :   List[str]  =    True
        _lowerCAmelCase       :   Tuple  =    0.763141

        _lowerCAmelCase       :   Optional[int]  =    TapasForQuestionAnswering(config=_lowerCamelCase   )
    elif task == "TABFACT":
        _lowerCAmelCase       :   Optional[Any]  =    TapasForSequenceClassification(config=_lowerCamelCase   )
    elif task == "MLM":
        _lowerCAmelCase       :   Union[str, Any]  =    TapasForMaskedLM(config=_lowerCamelCase   )
    elif task == "INTERMEDIATE_PRETRAINING":
        _lowerCAmelCase       :   List[Any]  =    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       :   List[Any]  =    TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt' ,   model_max_length=512   )
    tokenizer.save_pretrained(_lowerCamelCase   )

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


if __name__ == "__main__":
     _lowerCAmelCase						=		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."""
     )
     _lowerCAmelCase						=		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"""






from collections.abc import Callable







class 	__UpperCamelCase    :

  def __init__(		self    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   list  =    []
      # Stores indexes of each item for supporting updates and deletion.
      _lowerCAmelCase       :   dict  =    {}
      # Stores current size of heap.
      _lowerCAmelCase       :   Union[str, Any]  =    0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      _lowerCAmelCase       :   Union[str, Any]  =    key or (lambda _A      : x)

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return int((i - 1) / 2      ) if i > 0 else None

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    int(2 * i + 1      )
      return left if 0 < left < self.size else None

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    int(2 * i + 2      )
      return right if 0 < right < self.size else None

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    (
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    self.arr[j], self.arr[i]

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      return self.arr[i][1] < self.arr[j][1]

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self._left(_A      )
      _lowerCAmelCase       :   str  =    self._right(_A      )
      _lowerCAmelCase       :   Tuple  =    i

      if left is not None and not self._cmp(_A    ,_A      ):
          _lowerCAmelCase       :   int  =    left
      if right is not None and not self._cmp(_A    ,_A      ):
          _lowerCAmelCase       :   Optional[int]  =    right

      return valid_parent

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self._parent(_A      )
      while parent is not None and not self._cmp(_A    ,_A      ):
          self._swap(_A    ,_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   List[str]  =    parent, self._parent(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self._get_valid_parent(_A      )
      while valid_parent != index:
          self._swap(_A    ,_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    valid_parent, self._get_valid_parent(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      if item not in self.pos_map:
          return
      _lowerCAmelCase       :   int  =    self.pos_map[item]
      _lowerCAmelCase       :   Dict  =    [item, self.key(_A      )]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(_A      )
      self._heapify_down(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if item not in self.pos_map:
          return
      _lowerCAmelCase       :   List[str]  =    self.pos_map[item]
      del self.pos_map[item]
      _lowerCAmelCase       :   Dict  =    self.arr[self.size - 1]
      _lowerCAmelCase       :   Optional[Any]  =    index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
          self._heapify_up(_A      )
          self._heapify_down(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    len(self.arr      )
      if arr_len == self.size:
          self.arr.append([item, self.key(_A      )]      )
      else:
          _lowerCAmelCase       :   Any  =    [item, self.key(_A      )]
      _lowerCAmelCase       :   str  =    self.size
      self.size += 1
      self._heapify_up(self.size - 1      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.arr[0] if self.size else None







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.get_top()
      if top_item_tuple:
          self.delete_item(top_item_tuple[0]      )
      return top_item_tuple





def 		lowerCamelCase__		(					):

    '''simple docstring'''





if __name__ == "__main__":
 import doctest

 doctest.testmod()





