


'''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				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       int    							=							TapasConfig.from_json_file(UpperCamelCase__		)
				# set absolute/relative position embeddings parameter
				__magic_name__					:       Union[str, Any]    							=							reset_position_index_per_cell

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

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

								__magic_name__					:       Optional[int]    							=							TapasForQuestionAnswering(config=UpperCamelCase__		)
				elif task == "TABFACT":
								__magic_name__					:       Union[str, Any]    							=							TapasForSequenceClassification(config=UpperCamelCase__		)
				elif task == "MLM":
								__magic_name__					:       Any    							=							TapasForMaskedLM(config=UpperCamelCase__		)
				elif task == "INTERMEDIATE_PRETRAINING":
								__magic_name__					:       Optional[Any]    							=							TapasModel(config=UpperCamelCase__		)
				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(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		)

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

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

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


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							: 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."
		)
		_SCREAMING_SNAKE_CASE							: 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]))






