


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		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


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

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          __UpperCamelCase =			TapasForQuestionAnswering(config=_lowercase )
     elif task == "WTQ":
          # run_task_main.py hparams
          __UpperCamelCase =			4
          __UpperCamelCase =			True
          # hparam_utils.py hparams
          __UpperCamelCase =			0.66_46_94
          __UpperCamelCase =			0.20_79_51
          __UpperCamelCase =			0.12_11_94
          __UpperCamelCase =			True
          __UpperCamelCase =			True
          __UpperCamelCase =			False
          __UpperCamelCase =			0.0_35_25_13

          __UpperCamelCase =			TapasForQuestionAnswering(config=_lowercase )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          __UpperCamelCase =			4
          __UpperCamelCase =			False
          # hparam_utils.py hparams
          __UpperCamelCase =			36.45_19
          __UpperCamelCase =			0.90_34_21
          __UpperCamelCase =			2_22.0_88
          __UpperCamelCase =			True
          __UpperCamelCase =			True
          __UpperCamelCase =			True
          __UpperCamelCase =			0.76_31_41

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

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

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

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


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





class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			val
				snake_case_   		=			None
				snake_case_   		=			None







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if self.val:
					if val < self.val:
						if self.left is None:
							snake_case_   		=			Node(__UpperCamelCase				)
						else:
							self.left.insert(__UpperCamelCase				)
					elif val > self.val:
						if self.right is None:
							snake_case_   		=			Node(__UpperCamelCase				)
						else:
							self.right.insert(__UpperCamelCase				)
				else:
					snake_case_   		=			val







def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	# Recursive traversal
	if root:
		inorder(root.left      ,			lowercase__						)
		res.append(root.val						)
		inorder(root.right      ,			lowercase__						)





def a(lowercase__						):





	'''simple docstring'''
	# Build BST
	if len(lowercase__						) == 0:
		return arr
	snake_case_   		=			Node(arr[0]						)
	for i in range(1      ,			len(lowercase__						)						):
		root.insert(arr[i]						)
	# Traverse BST in order.
	snake_case_   		=			[]
	inorder(lowercase__      ,			lowercase__						)
	return res


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