"""simple docstring"""





def    lowercase_   (   _lowerCamelCase:	int = 50000000      ) ->      int:



							'''simple docstring'''

							__lowerCamelCase					:		int		   =     set()
							__lowerCamelCase					:		Any		   =     int((limit - 24) ** (1 / 2)      )

							__lowerCamelCase					:		Dict		   =     set(range(3						,   prime_square_limit + 1						,   2      )      )
							primes.add(2      )
							for p in range(3						,   prime_square_limit + 1						,   2      ):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p						,   prime_square_limit + 1						,   _lowerCamelCase      )      )      )

							for primea in primes:
														__lowerCamelCase					:		str		   =     primea * primea
														for primea in primes:
																					__lowerCamelCase					:		Optional[Any]		   =     primea * primea * primea
																					if square + cube >= limit - 16:
																												break
																					for primea in primes:
																												__lowerCamelCase					:		Optional[Any]		   =     primea * primea * primea * primea
																												__lowerCamelCase					:		Tuple		   =     square + cube + tetr
																												if total >= limit:
																																			break
																												ret.add(_lowerCamelCase      )

							return len(_lowerCamelCase      )


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





import argparse

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


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     TaForConditionalGeneration(_lowerCamelCase      )

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

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


if __name__ == "__main__":
				__A  =				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  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)