



"""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_   (   lowerCAmelCase__				:    Tuple   ,       lowerCAmelCase__				:    Union[str, Any]   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    Any						)						->       str:

  """simple docstring"""




  lowerCAmelCase_       :    Any								=			TapasConfig.from_json_file(lowerCAmelCase__						)
  # set absolute/relative position embeddings parameter
  lowerCAmelCase_       :    int								=			reset_position_index_per_cell

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

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

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

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

  # Save tokenizer files
  print(f"Save tokenizer files to {pytorch_dump_path}"						)
  lowerCAmelCase_       :    Union[str, Any]								=			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'   ,       model_max_length=512						)
  tokenizer.save_pretrained(lowerCAmelCase__						)

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


if __name__ == "__main__":
     lowercase__		:    Tuple				   =     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."""
     )
     lowercase__		:    Union[str, 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,
     )










"""simple docstring"""







import baseaa

def     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       bytes:

  """simple docstring"""




  return baseaa.aaaencode(string.encode('utf-8'						)						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    bytes						)						->       str:

  """simple docstring"""




  return baseaa.aaadecode(lowerCAmelCase__						).decode('utf-8'						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()





