





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_				( __a			,					__a			,					__a			,					__a			,					__a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[Any]    						=TapasConfig.from_json_file(__a	)
  # 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__:   Optional[int]    						=TapasForQuestionAnswering(config=__a	)
  elif task == "WTQ":
    # run_task_main.py hparams
    lowerCamelCase__:   Optional[Any]    						=4
    lowerCamelCase__:   List[Any]    						=True
    # hparam_utils.py hparams
    lowerCamelCase__:   Any    						=0.6_6_4_6_9_4
    lowerCamelCase__:   List[Any]    						=0.2_0_7_9_5_1
    lowerCamelCase__:   str    						=0.1_2_1_1_9_4
    lowerCamelCase__:   List[str]    						=True
    lowerCamelCase__:   Any    						=True
    lowerCamelCase__:   Dict    						=False
    lowerCamelCase__:   Optional[int]    						=0.0_3_5_2_5_1_3

    lowerCamelCase__:   Optional[int]    						=TapasForQuestionAnswering(config=__a	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    lowerCamelCase__:   Tuple    						=4
    lowerCamelCase__:   Optional[int]    						=False
    # hparam_utils.py hparams
    lowerCamelCase__:   int    						=36.4519
    lowerCamelCase__:   Any    						=0.9_0_3_4_2_1
    lowerCamelCase__:   Any    						=222.088
    lowerCamelCase__:   Optional[Any]    						=True
    lowerCamelCase__:   List[str]    						=True
    lowerCamelCase__:   Optional[Any]    						=True
    lowerCamelCase__:   List[str]    						=0.7_6_3_1_4_1

    lowerCamelCase__:   List[str]    						=TapasForQuestionAnswering(config=__a	)
  elif task == "TABFACT":
    lowerCamelCase__:   Union[str, Any]    						=TapasForSequenceClassification(config=__a	)
  elif task == "MLM":
    lowerCamelCase__:   Dict    						=TapasForMaskedLM(config=__a	)
  elif task == "INTERMEDIATE_PRETRAINING":
    lowerCamelCase__:   Any    						=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}"""	)
  lowerCamelCase__:   List[Any]    						=TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"			,					model_max_length=512	)
  tokenizer.save_pretrained(__a	)

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


if __name__ == "__main__":
      __A             =       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."
      )
      __A             =       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,
      )








import re





def   lowerCAmelCase_				( __a	)  ->	list:







  """simple docstring"""





  return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]"			,					str_	)]





def   lowerCAmelCase_				( __a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   Tuple    						=split_input(str_	)
  return "".join(
      ["".join([char.capitalize() for char in sub_str]	) for sub_str in string_split]	)





def   lowerCAmelCase_				( __a			,					__a			,					__a	)  ->	str:







  """simple docstring"""





  try:
    lowerCamelCase__:   Optional[Any]    						=split_input(__a	)
    if upper:
      lowerCamelCase__:   Tuple    						="".join(
          [
              separator.join([char.upper() for char in sub_str]	)
              for sub_str in string_split
          ]	)
    else:
      lowerCamelCase__:   Dict    						="".join(
          [
              separator.join([char.lower() for char in sub_str]	)
              for sub_str in string_split
          ]	)
    return res_str
  except IndexError:
    return "not valid string"





def   lowerCAmelCase_				( __a	)  ->	str:







  """simple docstring"""





  return to_simple_case(__a	)





def   lowerCAmelCase_				( __a	)  ->	str:







  """simple docstring"""





  try:
    lowerCamelCase__:   Any    						=to_simple_case(__a	)
    return res_str[0].lower() + res_str[1:]
  except IndexError:
    return "not valid string"





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  return to_complex_case(__a			,					__a			,					"_"	)





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  return to_complex_case(__a			,					__a			,					"-"	)


if __name__ == "__main__":
     __import__("doctest").testmod()

