






"""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       _A    (__a					,       __a					,       __a					,       __a					,       __a							)				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Tuple							=	TapasConfig.from_json_file(__a							)
    # set absolute/relative position embeddings parameter
    SCREAMING_SNAKE_CASE_   :						Any							=	reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        SCREAMING_SNAKE_CASE_   :						Any							=	TapasForQuestionAnswering(config=__a							)
    elif task == "WTQ":
        # run_task_main.py hparams
        SCREAMING_SNAKE_CASE_   :						List[str]							=	4
        SCREAMING_SNAKE_CASE_   :						Dict							=	True
        # hparam_utils.py hparams
        SCREAMING_SNAKE_CASE_   :						int							=	0.66_46_94
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	0.20_79_51
        SCREAMING_SNAKE_CASE_   :						Dict							=	0.12_11_94
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	True
        SCREAMING_SNAKE_CASE_   :						Dict							=	True
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	False
        SCREAMING_SNAKE_CASE_   :						Tuple							=	0.0_35_25_13

        SCREAMING_SNAKE_CASE_   :						Dict							=	TapasForQuestionAnswering(config=__a							)
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        SCREAMING_SNAKE_CASE_   :						Dict							=	4
        SCREAMING_SNAKE_CASE_   :						List[str]							=	False
        # hparam_utils.py hparams
        SCREAMING_SNAKE_CASE_   :						int							=	36.45_19
        SCREAMING_SNAKE_CASE_   :						int							=	0.90_34_21
        SCREAMING_SNAKE_CASE_   :						int							=	2_22.0_88
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	True
        SCREAMING_SNAKE_CASE_   :						Dict							=	True
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	True
        SCREAMING_SNAKE_CASE_   :						int							=	0.76_31_41

        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	TapasForQuestionAnswering(config=__a							)
    elif task == "TABFACT":
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	TapasForSequenceClassification(config=__a							)
    elif task == "MLM":
        SCREAMING_SNAKE_CASE_   :						Any							=	TapasForMaskedLM(config=__a							)
    elif task == "INTERMEDIATE_PRETRAINING":
        SCREAMING_SNAKE_CASE_   :						Dict							=	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}'							)
    SCREAMING_SNAKE_CASE_   :						Dict							=	TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + '''vocab.txt'''					,       model_max_length=5_12							)
    tokenizer.save_pretrained(__a							)

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


if __name__ == "__main__":
  UpperCAmelCase_     :				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."""
  )
  UpperCAmelCase_     :				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,
  )










"""simple docstring"""






from math import isqrt



def       _A    (__a							)				-> list[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[True] * max_number
    for i in range(2					,       isqrt(max_number - 1							) + 1							):
        if is_prime[i]:
            for j in range(i**2					,       __a					,       __a							):
                SCREAMING_SNAKE_CASE_   :						Tuple							=	False

    return [i for i in range(2					,       __a							) if is_prime[i]]



def       _A    (__a = 10**8							)				-> int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	calculate_prime_numbers(max_number // 2							)

    SCREAMING_SNAKE_CASE_   :						List[str]							=	0
    SCREAMING_SNAKE_CASE_   :						Any							=	0
    SCREAMING_SNAKE_CASE_   :						List[str]							=	len(__a							) - 1
    while left <= right:
        while prime_numbers[left] * prime_numbers[right] >= max_number:
            right -= 1
        semiprimes_count += right - left + 1
        left += 1

    return semiprimes_count


if __name__ == "__main__":
  print(f'''{solution() = }''')


