





'''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  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     List[str]     )   ->    int:
   # 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__     = TapasConfig.from_json_file(__lowerCamelCase     )
   # set absolute/relative position embeddings parameter
   a__     = reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      a__     = TapasForQuestionAnswering(config=__lowerCamelCase     )
   elif task == "WTQ":
      # run_task_main.py hparams
      a__     = 4
      a__     = True
      # hparam_utils.py hparams
      a__     = 0.664_694
      a__     = 0.207_951
      a__     = 0.121_194
      a__     = True
      a__     = True
      a__     = False
      a__     = 0.0_352_513

      a__     = TapasForQuestionAnswering(config=__lowerCamelCase     )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      a__     = 4
      a__     = False
      # hparam_utils.py hparams
      a__     = 36.4_519
      a__     = 0.903_421
      a__     = 222.088
      a__     = True
      a__     = True
      a__     = True
      a__     = 0.763_141

      a__     = TapasForQuestionAnswering(config=__lowerCamelCase     )
   elif task == "TABFACT":
      a__     = TapasForSequenceClassification(config=__lowerCamelCase     )
   elif task == "MLM":
      a__     = TapasForMaskedLM(config=__lowerCamelCase     )
   elif task == "INTERMEDIATE_PRETRAINING":
      a__     = 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__     = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"					,  model_max_length=512     )
   tokenizer.save_pretrained(__lowerCamelCase     )

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


if __name__ == "__main__":
  lowerCAmelCase_   :   List[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."
  )
  lowerCAmelCase_   :   Tuple       =					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'''


import argparse
import json

from tqdm import tqdm







def    _lowerCamelCase  ()   ->    Any:
			a__     = argparse.ArgumentParser()

			# Required parameters
			parser.add_argument(
			    "--src_path"					,  type=__lowerCamelCase					,  default="biencoder-nq-dev.json"					,  help="Path to raw DPR training data"					,  )
			parser.add_argument(
			    "--evaluation_set"					,  type=__lowerCamelCase					,  help="where to store parsed evaluation_set file"					,  )
			parser.add_argument(
			    "--gold_data_path"					,  type=__lowerCamelCase					,  help="where to store parsed gold_data_path file"					,  )
			a__     = parser.parse_args()

			with open(args.src_path					,  "r"     ) as src_file, open(args.evaluation_set					,  "w"     ) as eval_file, open(
			    args.gold_data_path					,  "w"     ) as gold_file:
						a__     = json.load(__lowerCamelCase     )
						for dpr_record in tqdm(__lowerCamelCase     ):
									a__     = dpr_record["question"]
									a__     = [context["title"] for context in dpr_record["positive_ctxs"]]
									eval_file.write(question + "\n"     )
									gold_file.write("\t".join(__lowerCamelCase     ) + "\n"     )


if __name__ == "__main__":
		main()



