






'''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 							lowercase_				(    __A :       List[str]					,      __A :       Tuple					,      __A :       Optional[int]					,      __A :       Tuple					,      __A :       Optional[Any]			)       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   List[str]				  =TapasConfig.from_json_file(__A			)
 # set absolute/relative position embeddings parameter
 lowercase					:   List[str]				  =reset_position_index_per_cell

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

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

  lowercase					:   Union[str, Any]				  =TapasForQuestionAnswering(config=__A			)
 elif task == "TABFACT":
  lowercase					:   Any				  =TapasForSequenceClassification(config=__A			)
 elif task == "MLM":
  lowercase					:   Optional[Any]				  =TapasForMaskedLM(config=__A			)
 elif task == "INTERMEDIATE_PRETRAINING":
  lowercase					:   List[str]				  =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					:   int				  =TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + '''vocab.txt'''					,      model_max_length=5_1_2			)
 tokenizer.save_pretrained(__A			)

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


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











'''simple docstring'''



def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   List[Any]				  =str(bin(__A			)			)
 binary_number += "0" * shift_amount
 return binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   Union[str, Any]				  =str(bin(__A			)			)[2:]
 if shift_amount >= len(__A			):
  return "0b0"
 lowercase					:   Any				  =binary_number[: len(__A			) - shift_amount]
 return "0b" + shifted_binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number >= 0:  # Get binary representation of positive number
  lowercase					:   str				  ='''0''' + str(bin(__A			)			).strip('''-'''			)[2:]
 else:  # Get binary (2's complement) representation of negative number
  lowercase					:   Dict				  =len(bin(__A			)[3:]			)  # Find 2's complement of number
  lowercase					:   Optional[Any]				  =bin(abs(__A			) - (1 << binary_number_length)			)[3:]
  lowercase					:   int				  =(
      '''1''' + '''0''' * (binary_number_length - len(__A			)) + binary_number
  )

 if shift_amount >= len(__A			):
  return "0b" + binary_number[0] * len(__A			)
 return (
     "0b"
     + binary_number[0] * shift_amount
     + binary_number[: len(__A			) - shift_amount]
 )


if __name__ == "__main__":
      import doctest

      doctest.testmod()



