





from __future__ import annotations




def 	lowerCAmelCase_					(						__UpperCAmelCase:    float	,     __UpperCAmelCase:    float	,     __UpperCAmelCase:    float   )   ->			float:
    if days_between_payments <= 0:
        raise ValueError('''days_between_payments must be > 0'''   )
    if daily_interest_rate < 0:
        raise ValueError('''daily_interest_rate must be >= 0'''   )
    if principal <= 0:
        raise ValueError('''principal must be > 0'''   )
    return principal * daily_interest_rate * days_between_payments




def 	lowerCAmelCase_					(						__UpperCAmelCase:    float	,     __UpperCAmelCase:    float	,     __UpperCAmelCase:    float	,     )   ->			float:
    if number_of_compounding_periods <= 0:
        raise ValueError('''number_of_compounding_periods must be > 0'''   )
    if nominal_annual_interest_rate_percentage < 0:
        raise ValueError('''nominal_annual_interest_rate_percentage must be >= 0'''   )
    if principal <= 0:
        raise ValueError('''principal must be > 0'''   )

    return principal * (
        (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
        - 1
    )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    float	,     __UpperCAmelCase:    float	,     __UpperCAmelCase:    float	,     )   ->			float:
    if number_of_years <= 0:
        raise ValueError('''number_of_years must be > 0'''   )
    if nominal_annual_percentage_rate < 0:
        raise ValueError('''nominal_annual_percentage_rate must be >= 0'''   )
    if principal <= 0:
        raise ValueError('''principal must be > 0'''   )

    return compound_interest(
        __UpperCAmelCase	,     nominal_annual_percentage_rate / 365	,     number_of_years * 365   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()








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_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    str   )   ->			Optional[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
    UpperCamelCase__							:       Any      =   TapasConfig.from_json_file(__UpperCAmelCase   )
    # set absolute/relative position embeddings parameter
    UpperCamelCase__							:       Optional[Any]      =   reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        UpperCamelCase__							:       List[Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WTQ":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   True
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   0.664694
        UpperCamelCase__							:       Union[str, Any]      =   0.207951
        UpperCamelCase__							:       Any      =   0.121194
        UpperCamelCase__							:       int      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       str      =   False
        UpperCamelCase__							:       int      =   0.0352513

        UpperCamelCase__							:       Dict      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   False
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   36.4519
        UpperCamelCase__							:       int      =   0.903421
        UpperCamelCase__							:       List[str]      =   222.088
        UpperCamelCase__							:       Dict      =   True
        UpperCamelCase__							:       List[str]      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       int      =   0.763141

        UpperCamelCase__							:       Union[str, Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "TABFACT":
        UpperCamelCase__							:       List[Any]      =   TapasForSequenceClassification(config=__UpperCAmelCase   )
    elif task == "MLM":
        UpperCamelCase__							:       Optional[Any]      =   TapasForMaskedLM(config=__UpperCAmelCase   )
    elif task == "INTERMEDIATE_PRETRAINING":
        UpperCamelCase__							:       str      =   TapasModel(config=__UpperCAmelCase   )
    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(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

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

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

    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,
  )

