





import math




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int   )   ->			list:
    UpperCamelCase__							:       Optional[int]      =   [True] * n
    UpperCamelCase__							:       Tuple      =   False
    UpperCamelCase__							:       Optional[Any]      =   False
    UpperCamelCase__							:       Optional[int]      =   True

    for i in range(3	,     int(n**0.5 + 1   )	,     2   ):
        UpperCamelCase__							:       Any      =   i * 2
        while index < n:
            UpperCamelCase__							:       str      =   False
            UpperCamelCase__							:       Union[str, Any]      =   index + i

    UpperCamelCase__							:       Optional[int]      =   [2]

    for i in range(3	,     __UpperCAmelCase	,     2   ):
        if is_prime[i]:
            primes.append(__UpperCAmelCase   )

    return primes




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int = 9999_6666_3333   )   ->			int:
    UpperCamelCase__							:       Tuple      =   math.floor(math.sqrt(__UpperCAmelCase   )   ) + 100
    UpperCamelCase__							:       List[Any]      =   prime_sieve(__UpperCAmelCase   )

    UpperCamelCase__							:       Optional[int]      =   0
    UpperCamelCase__							:       List[str]      =   0
    UpperCamelCase__							:       Tuple      =   primes[prime_index]

    while (last_prime**2) <= limit:
        UpperCamelCase__							:       Any      =   primes[prime_index + 1]

        UpperCamelCase__							:       Dict      =   last_prime**2
        UpperCamelCase__							:       List[str]      =   next_prime**2

        # Get numbers divisible by lps(current)
        UpperCamelCase__							:       Dict      =   lower_bound + last_prime
        while upper_bound > current <= limit:
            matches_sum += current
            current += last_prime

        # Reset the upper_bound
        while (upper_bound - next_prime) > limit:
            upper_bound -= next_prime

        # Add the numbers divisible by ups(current)
        UpperCamelCase__							:       Dict      =   upper_bound - next_prime
        while current > lower_bound:
            matches_sum += current
            current -= next_prime

        # Remove the numbers divisible by both ups and lps
        UpperCamelCase__							:       Tuple      =   0
        while upper_bound > current <= limit:
            if current <= lower_bound:
                # Increment the current number
                current += last_prime * next_prime
                continue

            if current > limit:
                break

            # Remove twice since it was added by both ups and lps
            matches_sum -= current * 2

            # Increment the current number
            current += last_prime * next_prime

        # Setup for next pair
        UpperCamelCase__							:       Union[str, Any]      =   next_prime
        prime_index += 1

    return matches_sum


if __name__ == "__main__":
  print(solution())








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,
  )

