






'''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 				snake_case       (  a_				:			Dict     , a_				:			int     , a_				:			Optional[int]     , a_				:			Dict     , a_				:			Dict						)							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: Union[str, Any]            =				TapasConfig.from_json_file(a_						)
      # set absolute/relative position embeddings parameter
      UpperCamelCase_					: List[Any]            =				reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            UpperCamelCase_					: str            =				TapasForQuestionAnswering(config=a_						)
      elif task == "WTQ":
            # run_task_main.py hparams
            UpperCamelCase_					: int            =				4
            UpperCamelCase_					: Optional[Any]            =				True
            # hparam_utils.py hparams
            UpperCamelCase_					: str            =				0.664_694
            UpperCamelCase_					: Tuple            =				0.207_951
            UpperCamelCase_					: Tuple            =				0.121_194
            UpperCamelCase_					: str            =				True
            UpperCamelCase_					: Dict            =				True
            UpperCamelCase_					: Union[str, Any]            =				False
            UpperCamelCase_					: List[Any]            =				0.0_352_513

            UpperCamelCase_					: Any            =				TapasForQuestionAnswering(config=a_						)
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            UpperCamelCase_					: Optional[int]            =				4
            UpperCamelCase_					: int            =				False
            # hparam_utils.py hparams
            UpperCamelCase_					: List[str]            =				36.4_519
            UpperCamelCase_					: Any            =				0.903_421
            UpperCamelCase_					: Dict            =				222.088
            UpperCamelCase_					: List[Any]            =				True
            UpperCamelCase_					: List[str]            =				True
            UpperCamelCase_					: Union[str, Any]            =				True
            UpperCamelCase_					: str            =				0.763_141

            UpperCamelCase_					: int            =				TapasForQuestionAnswering(config=a_						)
      elif task == "TABFACT":
            UpperCamelCase_					: Optional[Any]            =				TapasForSequenceClassification(config=a_						)
      elif task == "MLM":
            UpperCamelCase_					: Any            =				TapasForMaskedLM(config=a_						)
      elif task == "INTERMEDIATE_PRETRAINING":
            UpperCamelCase_					: Any            =				TapasModel(config=a_						)
      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(a_     , a_     , a_						)

      # Save pytorch-model (weights and configuration)
      print(f"Save PyTorch model to {pytorch_dump_path}"						)
      model.save_pretrained(a_						)

      # Save tokenizer files
      print(f"Save tokenizer files to {pytorch_dump_path}"						)
      UpperCamelCase_					: Tuple            =				TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""     , model_max_length=512						)
      tokenizer.save_pretrained(a_						)

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


if __name__ == "__main__":
      UpperCamelCase      					=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."
      )
      UpperCamelCase      					=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 __future__ import annotations

from collections import namedtuple



def 				snake_case       (  a_				:			float     , a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				namedtuple("""result"""     , """name value"""						)
      if (voltage, current, power).count(0						) != 1:
            raise ValueError("""Only one argument must be 0"""						)
      elif power < 0:
            raise ValueError(
                """Power cannot be negative in any electrical/electronics system"""						)
      elif voltage == 0:
            return result("""voltage"""     , power / current						)
      elif current == 0:
            return result("""current"""     , power / voltage						)
      elif power == 0:
            return result("""power"""     , float(round(abs(voltage * current						)     , 2						)						)						)
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






