import argparse

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


logging.set_verbosity_info()





def      __lowercase  (		lowerCamelCase      :  List[str]						,						lowerCamelCase      :  int						,						lowerCamelCase      :  Dict						,						lowerCamelCase      :  int						,						lowerCamelCase      :  Optional[Any]			):
 # 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[int]				=      bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 UpperCamelCase_      :		Optional[Any]				=      BertGenerationConfig(**lowerCamelCase			)
 if is_encoder:
  UpperCamelCase_      :		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__":
   a_					      = 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=50_358, type=int, help='Vocab size of model')
   a_					      = 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,
   )




def      __lowercase  (		lowerCamelCase      :  int = 998001			):
 UpperCamelCase_      :		int				=      0
 for i in range(999						,						99						,						-1			):  # 3 digit numbers range from 999 down to 100
  for j in range(999						,						99						,						-1			):
   UpperCamelCase_      :		str				=      str(i * j			)
   if product_string == product_string[::-1] and i * j < n:
    UpperCamelCase_      :		str				=      max(lowerCamelCase						,						i * j			)
 return answer


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



