

"""simple docstring"""






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def  UpperCamelCase   (     _lowerCAmelCase       :  List[Any],					_lowerCAmelCase       :  List[str],					_lowerCAmelCase       :  List[str],					_lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  Union[str, Any]	)			->     str:
	# Initialise PyTorch model
	_UpperCAmelCase      :							Union[str, Any]       =   BertConfig.from_pretrained(
	    """bert-large-cased""",					vocab_size=_lowerCAmelCase,					max_position_embeddings=512,					is_decoder=_lowerCAmelCase,					add_cross_attention=_lowerCAmelCase,					)
	_UpperCAmelCase      :							Optional[Any]       =   bert_config.to_dict()
	del bert_config_dict["type_vocab_size"]
	_UpperCAmelCase      :							List[str]       =   BertGenerationConfig(**_lowerCAmelCase	)
	if is_encoder:
		_UpperCAmelCase      :							Optional[int]       =   BertGenerationEncoder(_lowerCAmelCase	)
	else:
		_UpperCAmelCase      :							List[str]       =   BertGenerationDecoder(_lowerCAmelCase	)
	print(f'''Building PyTorch model from configuration: {config}'''	)

	# Load weights from tf checkpoint
	load_tf_weights_in_bert_generation(
	    _lowerCAmelCase,					_lowerCAmelCase,					model_class="""bert""",					is_encoder_named_decoder=_lowerCAmelCase,					is_encoder=_lowerCAmelCase,					)

	# Save pytorch-model
	print(f'''Save PyTorch model and config to {pytorch_dump_path}'''	)
	model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
						lowerCamelCase__			:					Optional[Any]							  =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--is_encoder_named_decoder''',
						    action='''store_true''',
						    help='''If decoder has to be renamed to encoder in PyTorch model.''',
						)
						parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
						parser.add_argument('''--vocab_size''', default=5_03_58, type=int, help='''Vocab size of model''')
						lowerCamelCase__			:					int							  =					parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_hub_path,
						    args.pytorch_dump_path,
						    args.is_encoder_named_decoder,
						    args.vocab_size,
						    is_encoder=args.is_encoder,
						)








"""simple docstring"""






import math
def  UpperCamelCase   (     _lowerCAmelCase       :  int	)			->     bool:

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5,					int(math.sqrt(_lowerCAmelCase	) + 1	),					6	):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True
def  UpperCamelCase   (     _lowerCAmelCase       :  int = 2000000	)			->     int:
	return sum(num for num in range(3,					_lowerCAmelCase,					2	) if is_prime(_lowerCAmelCase	)	) + 2 if n > 2 else 0


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





