




'''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_(	snake_case							:       Union[str, Any]							,				snake_case							:       List[Any]							,				snake_case							:       List[Any]							,				snake_case							:       Union[str, Any]							,				snake_case							:       Dict     ):

							'''simple docstring'''






							snake_case_						=      TapasConfig.from_json_file(snake_case     )
							# set absolute/relative position embeddings parameter
							snake_case_						=      reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														snake_case_						=      TapasForQuestionAnswering(config=snake_case     )
							elif task == "WTQ":
														# run_task_main.py hparams
														snake_case_						=      4
														snake_case_						=      True
														# hparam_utils.py hparams
														snake_case_						=      0.664694
														snake_case_						=      0.207951
														snake_case_						=      0.121194
														snake_case_						=      True
														snake_case_						=      True
														snake_case_						=      False
														snake_case_						=      0.0352513

														snake_case_						=      TapasForQuestionAnswering(config=snake_case     )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														snake_case_						=      4
														snake_case_						=      False
														# hparam_utils.py hparams
														snake_case_						=      36.4519
														snake_case_						=      0.903421
														snake_case_						=      2_2_2.0_8_8
														snake_case_						=      True
														snake_case_						=      True
														snake_case_						=      True
														snake_case_						=      0.763141

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

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

							# Save tokenizer files
							print(f'Save tokenizer files to {pytorch_dump_path}'     )
							snake_case_						=      TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + "vocab.txt"							,				model_max_length=5_1_2     )
							tokenizer.save_pretrained(snake_case     )

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


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   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."
		)
		_SCREAMING_SNAKE_CASE							:   Dict      		= 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 math import pow






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,):







  """simple docstring"""
  if current_sum == needed_sum:
    # If the sum of the powers is equal to needed_sum, then we have a solution.
    solutions_count += 1
    return current_sum, solutions_count

  _UpperCAmelCase         =					int(pow(lowercase      ,lowercase						)						)
  if current_sum + i_to_n <= needed_sum:
    # If the sum of the powers is less than needed_sum, then continue adding powers.
    current_sum += i_to_n
    _UpperCAmelCase       , _UpperCAmelCase         =					backtrack(
        lowercase      ,lowercase      ,current_number + 1      ,lowercase      ,lowercase						)
    current_sum -= i_to_n
  if i_to_n < needed_sum:
    # If the power of i is less than needed_sum, then try with the next power.
    _UpperCAmelCase       , _UpperCAmelCase         =					backtrack(
        lowercase      ,lowercase      ,current_number + 1      ,lowercase      ,lowercase						)
  return current_sum, solutions_count






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
    raise ValueError(
        """Invalid input\n"""
        """needed_sum must be between 1 and 1000, power between 2 and 10."""						)

  return backtrack(lowercase      ,lowercase      ,1      ,0      ,0						)[1]  # Return the solutions_count


if __name__ == "__main__":
    import doctest

    doctest.testmod()
