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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict ,     __UpperCamelCase    :       Dict ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Dict ,     __UpperCamelCase    :       List[str]	)							->					Optional[int]:







  """simple docstring"""





  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__										=      0.66_4694
    SCREAMING_SNAKE_CASE__										=      0.20_7951
    SCREAMING_SNAKE_CASE__										=      0.12_1194
    SCREAMING_SNAKE_CASE__										=      True
    SCREAMING_SNAKE_CASE__										=      True
    SCREAMING_SNAKE_CASE__										=      False
    SCREAMING_SNAKE_CASE__										=      0.035_2513

    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__										=      36.4519
    SCREAMING_SNAKE_CASE__										=      0.90_3421
    SCREAMING_SNAKE_CASE__										=      222.088
    SCREAMING_SNAKE_CASE__										=      True
    SCREAMING_SNAKE_CASE__										=      True
    SCREAMING_SNAKE_CASE__										=      True
    SCREAMING_SNAKE_CASE__										=      0.76_3141

    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			: 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.'''
   )
   __lowerCamelCase			: Dict							   =		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 warnings

from ..trainer import Trainer
from ..utils import logging


__lowerCamelCase			: List[Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				Tuple       ,	_lowercase			:				Optional[int]=None       ,	**_lowercase			:				List[Any]      ):



     """simple docstring"""






     warnings.warn(
         """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
         """instead."""       ,	_lowercase       ,	)
     super().__init__(args=_lowercase       ,	**_lowercase      )
