
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__							:    int  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Tuple  ):
 # 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
 __a			:  int		    =  TapasConfig.from_json_file(lowerCAmelCase__  )
 # set absolute/relative position embeddings parameter
 __a			:  Any		    =  reset_position_index_per_cell

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  __a			:  Union[str, Any]		    =  TapasForQuestionAnswering(config=lowerCAmelCase__  )
 elif task == "WTQ":
  # run_task_main.py hparams
  __a			:  Optional[Any]		    =  4
  __a			:  Tuple		    =  True
  # hparam_utils.py hparams
  __a			:  Optional[int]		    =  0.66_46_94
  __a			:  str		    =  0.20_79_51
  __a			:  List[str]		    =  0.12_11_94
  __a			:  Tuple		    =  True
  __a			:  Any		    =  True
  __a			:  int		    =  False
  __a			:  Union[str, Any]		    =  0.0_35_25_13

  __a			:  List[Any]		    =  TapasForQuestionAnswering(config=lowerCAmelCase__  )
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  __a			:  Any		    =  4
  __a			:  List[Any]		    =  False
  # hparam_utils.py hparams
  __a			:  Union[str, Any]		    =  36.45_19
  __a			:  str		    =  0.90_34_21
  __a			:  Optional[int]		    =  2_2_2.0_8_8
  __a			:  str		    =  True
  __a			:  Any		    =  True
  __a			:  Optional[Any]		    =  True
  __a			:  List[str]		    =  0.76_31_41

  __a			:  List[str]		    =  TapasForQuestionAnswering(config=lowerCAmelCase__  )
 elif task == "TABFACT":
  __a			:  Optional[int]		    =  TapasForSequenceClassification(config=lowerCAmelCase__  )
 elif task == "MLM":
  __a			:  int		    =  TapasForMaskedLM(config=lowerCAmelCase__  )
 elif task == "INTERMEDIATE_PRETRAINING":
  __a			:  Union[str, Any]		    =  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}"  )
 __a			:  List[Any]		    =  TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + '''vocab.txt'''  ,       model_max_length=5_1_2  )
 tokenizer.save_pretrained(lowerCAmelCase__  )

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


if __name__ == "__main__":
      lowercase__          =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__          =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,
      )


from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
