

from collections.abc import Sequence
from queue import Queue





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None							,     lowerCAmelCase=None      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =start
        _lowercase			 =end
        _lowercase			 =val
        _lowercase			 =(start + end) // 2
        _lowercase			 =left
        _lowercase			 =right




  def __repr__(       self      )						->				Dict:
        '''simple docstring'''






        return F'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =collection
        _lowercase			 =function
        if self.collection:
              _lowercase			 =self._build_tree(0							,     len(lowerCAmelCase      ) - 1      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Optional[int]:
        '''simple docstring'''






        self._update_tree(self.root							,     lowerCAmelCase							,     lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Tuple:
        '''simple docstring'''






        return self._query_range(self.root							,     lowerCAmelCase							,     lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        if start == end:
              return SegmentTreeNode(lowerCAmelCase							,     lowerCAmelCase							,     self.collection[start]      )
        _lowercase			 =(start + end) // 2
        _lowercase			 =self._build_tree(lowerCAmelCase							,     lowerCAmelCase      )
        _lowercase			 =self._build_tree(mid + 1							,     lowerCAmelCase      )
        return SegmentTreeNode(lowerCAmelCase							,     lowerCAmelCase							,     self.fn(left.val							,     right.val      )							,     lowerCAmelCase							,     lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Union[str, Any]:
        '''simple docstring'''






        if node.start == i and node.end == i:
              _lowercase			 =val
              return
        if i <= node.mid:
              self._update_tree(node.left							,     lowerCAmelCase							,     lowerCAmelCase      )
        else:
              self._update_tree(node.right							,     lowerCAmelCase							,     lowerCAmelCase      )
        _lowercase			 =self.fn(node.left.val							,     node.right.val      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Optional[int]:
        '''simple docstring'''






        if node.start == i and node.end == j:
              return node.val

        if i <= node.mid:
              if j <= node.mid:
                    # range in left child tree
                    return self._query_range(node.left							,     lowerCAmelCase							,     lowerCAmelCase      )
              else:
                    # range in left child tree and right child tree
                    return self.fn(
                        self._query_range(node.left							,     lowerCAmelCase							,     node.mid      )							,     self._query_range(node.right							,     node.mid + 1							,     lowerCAmelCase      )							,     )
        else:
              # range in right child tree
              return self._query_range(node.right							,     lowerCAmelCase							,     lowerCAmelCase      )




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        if self.root is not None:
              _lowercase			 =Queue()
              queue.put(self.root      )
              while not queue.empty():
                    _lowercase			 =queue.get()
                    yield node

                    if node.left is not None:
                          queue.put(node.left      )

                    if node.right is not None:
                          queue.put(node.right      )


if __name__ == "__main__":
  import operator

  for fn in [operator.add, max, min]:
    print('*' * 5_0)
    lowercase_             =     SegmentTree([2, 1, 5, 3, 4], fn)
    for node in arr.traverse():
      print(node)
    print()

    arr.update(1, 5)
    for node in arr.traverse():
      print(node)
    print()

    print(arr.query_range(3, 4))  # 7
    print(arr.query_range(2, 2))  # 5
    print(arr.query_range(1, 3))  # 13
    print()





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   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =TapasConfig.from_json_file(A__  )
      # set absolute/relative position embeddings parameter
      _lowercase			 =reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WTQ":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =True
            # hparam_utils.py hparams
            _lowercase			 =0.664694
            _lowercase			 =0.207951
            _lowercase			 =0.121194
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =False
            _lowercase			 =0.0352513

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =False
            # hparam_utils.py hparams
            _lowercase			 =36.4519
            _lowercase			 =0.903421
            _lowercase			 =222.088
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =0.763141

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "TABFACT":
            _lowercase			 =TapasForSequenceClassification(config=A__  )
      elif task == "MLM":
            _lowercase			 =TapasForMaskedLM(config=A__  )
      elif task == "INTERMEDIATE_PRETRAINING":
            _lowercase			 =TapasModel(config=A__  )
      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(A__   ,     A__   ,     A__  )

      # Save pytorch-model (weights and configuration)
      print(F'''Save PyTorch model to {pytorch_dump_path}'''  )
      model.save_pretrained(A__  )

      # Save tokenizer files
      print(F'''Save tokenizer files to {pytorch_dump_path}'''  )
      _lowercase			 =TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'   ,     model_max_length=512  )
      tokenizer.save_pretrained(A__  )

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


if __name__ == "__main__":
  lowercase_             =     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.'
  )
  lowercase_             =     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,
  )


