



from math import isqrt, loga


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		[True] * max_number
 for i in range(2 ,					isqrt(max_number - 1					) + 1					):
  if is_prime[i]:
   for j in range(i**2 ,					UpperCAmelCase__ ,					UpperCAmelCase__					):
    a						=		False

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int = 80_08_00 ,					UpperCAmelCase__ :int = 80_08_00					):






 '''simple docstring'''

 a						=		degree * loga(UpperCAmelCase__					)
 a						=		int(UpperCAmelCase__					)
 a						=		calculate_prime_numbers(UpperCAmelCase__					)

 a						=		0
 a						=		0
 a						=		len(UpperCAmelCase__					) - 1
 while left < right:
  while (
      prime_numbers[right] * loga(prime_numbers[left]					)
      + prime_numbers[left] * loga(prime_numbers[right]					)
      > upper_bound
  ):
   right -= 1
  hybrid_integers_count += right - left
  left += 1

 return hybrid_integers_count


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






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     Union[str, Any]        =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

