





"""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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						TapasConfig.from_json_file(UpperCAmelCase_  )
  # set absolute/relative position embeddings parameter
  __UpperCAmelCase    :						Union[str, Any]       =						reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    __UpperCAmelCase    :						Optional[Any]       =						TapasForQuestionAnswering(config=UpperCAmelCase_  )
  elif task == "WTQ":
    # run_task_main.py hparams
    __UpperCAmelCase    :						str       =						4
    __UpperCAmelCase    :						Any       =						True
    # hparam_utils.py hparams
    __UpperCAmelCase    :						Optional[int]       =						0.664_694
    __UpperCAmelCase    :						str       =						0.207_951
    __UpperCAmelCase    :						List[Any]       =						0.121_194
    __UpperCAmelCase    :						int       =						True
    __UpperCAmelCase    :						Optional[int]       =						True
    __UpperCAmelCase    :						int       =						False
    __UpperCAmelCase    :						int       =						0.0_352_513

    __UpperCAmelCase    :						Optional[Any]       =						TapasForQuestionAnswering(config=UpperCAmelCase_  )
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    __UpperCAmelCase    :						Dict       =						4
    __UpperCAmelCase    :						List[Any]       =						False
    # hparam_utils.py hparams
    __UpperCAmelCase    :						Optional[Any]       =						36.4_519
    __UpperCAmelCase    :						Dict       =						0.903_421
    __UpperCAmelCase    :						List[Any]       =						222.088
    __UpperCAmelCase    :						Any       =						True
    __UpperCAmelCase    :						Optional[Any]       =						True
    __UpperCAmelCase    :						List[Any]       =						True
    __UpperCAmelCase    :						List[str]       =						0.763_141

    __UpperCAmelCase    :						Tuple       =						TapasForQuestionAnswering(config=UpperCAmelCase_  )
  elif task == "TABFACT":
    __UpperCAmelCase    :						Union[str, Any]       =						TapasForSequenceClassification(config=UpperCAmelCase_  )
  elif task == "MLM":
    __UpperCAmelCase    :						List[str]       =						TapasForMaskedLM(config=UpperCAmelCase_  )
  elif task == "INTERMEDIATE_PRETRAINING":
    __UpperCAmelCase    :						Optional[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}'''  )
  __UpperCAmelCase    :						Dict       =						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__":
  lowercase__				: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.'
  )
  lowercase__				:Optional[int]				   =       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"""





# flake8: noqa
# Lint as: python3
lowercase__				:Tuple				   =       [
    'VerificationMode',
    'Version',
    'disable_progress_bar',
    'enable_progress_bar',
    'is_progress_bar_enabled',
    'experimental',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental