




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						:	str			,			__UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	List[str]			,			__UpperCAmelCase						:	Any							)					-> int:
      # Initialise PyTorch model.
      # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
      # TapasConfig to False.

      # initialize configuration from json file
      SCREAMING_SNAKE_CASE_							=			TapasConfig.from_json_file(__UpperCAmelCase							)
      # set absolute/relative position embeddings parameter
      SCREAMING_SNAKE_CASE_							=			reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            SCREAMING_SNAKE_CASE_							=			TapasForQuestionAnswering(config=__UpperCAmelCase							)
      elif task == "WTQ":
            # run_task_main.py hparams
            SCREAMING_SNAKE_CASE_							=			4
            SCREAMING_SNAKE_CASE_							=			True
            # hparam_utils.py hparams
            SCREAMING_SNAKE_CASE_							=			0.6_6_4_6_9_4
            SCREAMING_SNAKE_CASE_							=			0.2_0_7_9_5_1
            SCREAMING_SNAKE_CASE_							=			0.1_2_1_1_9_4
            SCREAMING_SNAKE_CASE_							=			True
            SCREAMING_SNAKE_CASE_							=			True
            SCREAMING_SNAKE_CASE_							=			False
            SCREAMING_SNAKE_CASE_							=			0.0_3_5_2_5_1_3

            SCREAMING_SNAKE_CASE_							=			TapasForQuestionAnswering(config=__UpperCAmelCase							)
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            SCREAMING_SNAKE_CASE_							=			4
            SCREAMING_SNAKE_CASE_							=			False
            # hparam_utils.py hparams
            SCREAMING_SNAKE_CASE_							=			3_6.4_5_1_9
            SCREAMING_SNAKE_CASE_							=			0.9_0_3_4_2_1
            SCREAMING_SNAKE_CASE_							=			2_2_2.0_8_8
            SCREAMING_SNAKE_CASE_							=			True
            SCREAMING_SNAKE_CASE_							=			True
            SCREAMING_SNAKE_CASE_							=			True
            SCREAMING_SNAKE_CASE_							=			0.7_6_3_1_4_1

            SCREAMING_SNAKE_CASE_							=			TapasForQuestionAnswering(config=__UpperCAmelCase							)
      elif task == "TABFACT":
            SCREAMING_SNAKE_CASE_							=			TapasForSequenceClassification(config=__UpperCAmelCase							)
      elif task == "MLM":
            SCREAMING_SNAKE_CASE_							=			TapasForMaskedLM(config=__UpperCAmelCase							)
      elif task == "INTERMEDIATE_PRETRAINING":
            SCREAMING_SNAKE_CASE_							=			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}"							)
      SCREAMING_SNAKE_CASE_							=			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'			,			model_max_length=5_12							)
      tokenizer.save_pretrained(__UpperCAmelCase							)

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


if __name__ == "__main__":
   lowerCamelCase__ :   Union[str, Any]					=							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.'
   )
   lowerCamelCase__ :   str					=							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 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return number | (1 << position)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return number & ~(1 << position)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return number ^ (1 << position)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> bool:
      return ((number >> position) & 1) == 1

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	int							)					-> int:
      return int((number & (1 << position)) != 0							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()