
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						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  lowercase__	: List[str]     		=   TapasConfig.from_json_file(_SCREAMING_SNAKE_CASE )
  # set absolute/relative position embeddings parameter
  lowercase__	: Dict     		=   reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    lowercase__	: Union[str, Any]     		=   TapasForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
  elif task == "WTQ":
    # run_task_main.py hparams
    lowercase__	: Tuple     		=   4
    lowercase__	: List[Any]     		=   True
    # hparam_utils.py hparams
    lowercase__	: Tuple     		=   0.66_4694
    lowercase__	: Dict     		=   0.20_7951
    lowercase__	: str     		=   0.12_1194
    lowercase__	: Optional[int]     		=   True
    lowercase__	: List[str]     		=   True
    lowercase__	: int     		=   False
    lowercase__	: int     		=   0.035_2513

    lowercase__	: Dict     		=   TapasForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    lowercase__	: List[str]     		=   4
    lowercase__	: str     		=   False
    # hparam_utils.py hparams
    lowercase__	: List[str]     		=   36.4519
    lowercase__	: Optional[Any]     		=   0.90_3421
    lowercase__	: Tuple     		=   222.088
    lowercase__	: Any     		=   True
    lowercase__	: str     		=   True
    lowercase__	: int     		=   True
    lowercase__	: Any     		=   0.76_3141

    lowercase__	: str     		=   TapasForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
  elif task == "TABFACT":
    lowercase__	: List[Any]     		=   TapasForSequenceClassification(config=_SCREAMING_SNAKE_CASE )
  elif task == "MLM":
    lowercase__	: Optional[int]     		=   TapasForMaskedLM(config=_SCREAMING_SNAKE_CASE )
  elif task == "INTERMEDIATE_PRETRAINING":
    lowercase__	: List[Any]     		=   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}' )
  lowercase__	: List[Any]     		=   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__":
     lowerCamelCase__     :		List[str]        = 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__     :		List[Any]        = 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 __future__ import annotations

SCREAMING_SNAKE_CASE		:     Tuple         =	1.6_021E-19  # units = C






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, ):
						if (conductivity, electron_conc, mobility).count(0			) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""			)
						elif conductivity < 0:
												raise ValueError("""Conductivity cannot be negative"""			)
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative"""			)
						elif mobility < 0:
												raise ValueError("""mobility cannot be negative"""			)
						elif conductivity == 0:
												return (
												    "conductivity",
												    mobility * electron_conc * ELECTRON_CHARGE,
												)
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    conductivity / (mobility * ELECTRON_CHARGE),
												)
						else:
												return (
												    "mobility",
												    conductivity / (electron_conc * ELECTRON_CHARGE),
												)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






