


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		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						Union[str, Any]		):




      """simple docstring"""





      a		:Tuple									=			TapasConfig.from_json_file(UpperCAmelCase_		)
      # set absolute/relative position embeddings parameter
      a		:List[Any]									=			reset_position_index_per_cell

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

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

            a		:int									=			TapasForQuestionAnswering(config=UpperCAmelCase_		)
      elif task == "TABFACT":
            a		:Tuple									=			TapasForSequenceClassification(config=UpperCAmelCase_		)
      elif task == "MLM":
            a		:Tuple									=			TapasForMaskedLM(config=UpperCAmelCase_		)
      elif task == "INTERMEDIATE_PRETRAINING":
            a		:int									=			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}'''		)
      a		:Tuple									=			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__":
     snake_case							:       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.'''
     )
     snake_case							:       Any										=	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,
     )









def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      if b == 0:
            return 1
      if (b % 2) == 0:
            return actual_power(UpperCAmelCase_					,    int(b / 2		)		) * actual_power(UpperCAmelCase_					,    int(b / 2		)		)
      else:
            return a * actual_power(UpperCAmelCase_					,    int(b / 2		)		) * actual_power(UpperCAmelCase_					,    int(b / 2		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      if b < 0:
            return 1 / actual_power(UpperCAmelCase_					,    UpperCAmelCase_		)
      return actual_power(UpperCAmelCase_					,    UpperCAmelCase_		)


if __name__ == "__main__":
     print(power(-2, -3))





