





"""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__							(  SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						List[str]							,						SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      lowerCAmelCase  						=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE			)
      # set absolute/relative position embeddings parameter
      lowerCAmelCase  						=       reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            lowerCAmelCase  						=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE			)
      elif task == "WTQ":
            # run_task_main.py hparams
            lowerCAmelCase  						=       4
            lowerCAmelCase  						=       True
            # hparam_utils.py hparams
            lowerCAmelCase  						=       0.66_46_94
            lowerCAmelCase  						=       0.20_79_51
            lowerCAmelCase  						=       0.12_11_94
            lowerCAmelCase  						=       True
            lowerCAmelCase  						=       True
            lowerCAmelCase  						=       False
            lowerCAmelCase  						=       0.0_35_25_13

            lowerCAmelCase  						=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE			)
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            lowerCAmelCase  						=       4
            lowerCAmelCase  						=       False
            # hparam_utils.py hparams
            lowerCAmelCase  						=       36.45_19
            lowerCAmelCase  						=       0.90_34_21
            lowerCAmelCase  						=       2_22.0_88
            lowerCAmelCase  						=       True
            lowerCAmelCase  						=       True
            lowerCAmelCase  						=       True
            lowerCAmelCase  						=       0.76_31_41

            lowerCAmelCase  						=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE			)
      elif task == "TABFACT":
            lowerCAmelCase  						=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE			)
      elif task == "MLM":
            lowerCAmelCase  						=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE			)
      elif task == "INTERMEDIATE_PRETRAINING":
            lowerCAmelCase  						=       TapasModel(config=SCREAMING_SNAKE_CASE			)
      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(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      # Save pytorch-model (weights and configuration)
      print(F'Save PyTorch model to {pytorch_dump_path}'			)
      model.save_pretrained(SCREAMING_SNAKE_CASE			)

      # Save tokenizer files
      print(F'Save tokenizer files to {pytorch_dump_path}'			)
      lowerCAmelCase  						=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""							,						model_max_length=5_12			)
      tokenizer.save_pretrained(SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   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."
       )
       SCREAMING_SNAKE_CASE__					=   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"""





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=_UpperCAmelCase					)
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      field(default='language-modeling'     ,  metadata={'include_in_asdict_even_if_is_default': True}					)

      _SCREAMING_SNAKE_CASE					=      Features({'text': Value('string'					)}					)
      _SCREAMING_SNAKE_CASE					=      Features({}					)
      _SCREAMING_SNAKE_CASE					=      "text"





      @property
      def     _snake_case			(       self )		->     Dict[str, str]:
            return {self.text_column: "text"}






