




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor




@require_tokenizers
@require_vision
class 		__lowercase						(unittest.TestCase				):




      """simple docstring"""





      def       UpperCAmelCase ( self      )						->							Union[str, Any]:
         snake_case :							Optional[int]           =			tempfile.mkdtemp()

         # fmt: off
         snake_case :							Any           =			["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
         # fmt: on
         snake_case :							int           =			os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["""vocab_file"""]      )
         with open(self.vocab_file							, """w"""							, encoding="""utf-8"""      ) as vocab_writer:
            vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]      )      )

         snake_case :							Optional[Any]           =			{
             """do_resize""": True,
             """size""": {"""height""": 1_8, """width""": 1_8},
             """do_normalize""": True,
             """image_mean""": [0.5, 0.5, 0.5],
             """image_std""": [0.5, 0.5, 0.5],
         }
         snake_case :							Any           =			os.path.join(self.tmpdirname							, _UpperCamelCase      )
         with open(self.image_processor_file							, """w"""							, encoding="""utf-8"""      ) as fp:
            json.dump(_UpperCamelCase							, _UpperCamelCase      )

      def       UpperCAmelCase ( self							, **A      )						->							Optional[int]:
         return BertTokenizer.from_pretrained(self.tmpdirname							, **_UpperCamelCase      )

      def       UpperCAmelCase ( self							, **A      )						->							Dict:
         return ViTImageProcessor.from_pretrained(self.tmpdirname							, **_UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							int:
         shutil.rmtree(self.tmpdirname      )

      def       UpperCAmelCase ( self      )						->							Any:
         snake_case :							List[str]           =			[np.random.randint(2_5_5							, size=(3, 3_0, 4_0_0)							, dtype=np.uinta      )]

         snake_case :							Tuple           =			[Image.fromarray(np.moveaxis(_UpperCamelCase							, 0							, -1      )      ) for x in image_inputs]

         return image_inputs

      def       UpperCAmelCase ( self      )						->							Optional[int]:
         snake_case :							str           =			self.get_tokenizer()
         snake_case :							str           =			self.get_image_processor()

         snake_case :							Any           =			VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							, image_processor=_UpperCamelCase      )

         processor.save_pretrained(self.tmpdirname      )
         snake_case :							Tuple           =			VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname      )

         self.assertEqual(processor.tokenizer.get_vocab()							, tokenizer.get_vocab()      )
         self.assertIsInstance(processor.tokenizer							, (BertTokenizer, BertTokenizerFast)      )

         self.assertEqual(processor.image_processor.to_json_string()							, image_processor.to_json_string()      )
         self.assertIsInstance(processor.image_processor							, _UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							Dict:
         snake_case :							List[Any]           =			VisionTextDualEncoderProcessor(
             tokenizer=self.get_tokenizer()							, image_processor=self.get_image_processor()      )
         processor.save_pretrained(self.tmpdirname      )

         snake_case :							Dict           =			self.get_tokenizer(bos_token="""(BOS)"""							, eos_token="""(EOS)"""      )
         snake_case :							Optional[Any]           =			self.get_image_processor(do_normalize=_UpperCamelCase							, padding_value=1.0      )

         snake_case :							Optional[int]           =			VisionTextDualEncoderProcessor.from_pretrained(
             self.tmpdirname							, bos_token="""(BOS)"""							, eos_token="""(EOS)"""							, do_normalize=_UpperCamelCase							, padding_value=1.0      )

         self.assertEqual(processor.tokenizer.get_vocab()							, tokenizer_add_kwargs.get_vocab()      )
         self.assertIsInstance(processor.tokenizer							, (BertTokenizer, BertTokenizerFast)      )

         self.assertEqual(processor.image_processor.to_json_string()							, image_processor_add_kwargs.to_json_string()      )
         self.assertIsInstance(processor.image_processor							, _UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							Dict:
         snake_case :							List[str]           =			self.get_image_processor()
         snake_case :							Optional[Any]           =			self.get_tokenizer()

         snake_case :							Optional[Any]           =			VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							, image_processor=_UpperCamelCase      )

         snake_case :							Optional[Any]           =			self.prepare_image_inputs()

         snake_case :							List[Any]           =			image_processor(_UpperCamelCase							, return_tensors="""np"""      )
         snake_case :							Any           =			processor(images=_UpperCamelCase							, return_tensors="""np"""      )

         for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum()							, input_processor[key].sum()							, delta=1e-2      )

      def       UpperCAmelCase ( self      )						->							Tuple:
         snake_case :							List[str]           =			self.get_image_processor()
         snake_case :							Union[str, Any]           =			self.get_tokenizer()

         snake_case :							Optional[int]           =			VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							, image_processor=_UpperCamelCase      )

         snake_case :							Dict           =			"""lower newer"""

         snake_case :							List[str]           =			processor(text=_UpperCamelCase      )

         snake_case :							Tuple           =			tokenizer(_UpperCamelCase      )

         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]							, encoded_processor[key]      )

      def       UpperCAmelCase ( self      )						->							Optional[Any]:
         snake_case :							Any           =			self.get_image_processor()
         snake_case :							List[Any]           =			self.get_tokenizer()

         snake_case :							str           =			VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							, image_processor=_UpperCamelCase      )

         snake_case :							Tuple           =			"""lower newer"""
         snake_case :							Optional[int]           =			self.prepare_image_inputs()

         snake_case :							Any           =			processor(text=_UpperCamelCase							, images=_UpperCamelCase      )

         self.assertListEqual(list(inputs.keys()      )							, ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]      )

         # test if it raises when no input is passed
         with self.assertRaises(_UpperCamelCase      ):
            processor()

      def       UpperCAmelCase ( self      )						->							Dict:
         snake_case :							Union[str, Any]           =			self.get_image_processor()
         snake_case :							Dict           =			self.get_tokenizer()

         snake_case :							Optional[Any]           =			VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							, image_processor=_UpperCamelCase      )

         snake_case :							Optional[int]           =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case :							List[str]           =			processor.batch_decode(_UpperCamelCase      )
         snake_case :							Union[str, Any]           =			tokenizer.batch_decode(_UpperCamelCase      )

         self.assertListEqual(_UpperCamelCase							, _UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							List[Any]:
         snake_case :							Optional[int]           =			self.get_image_processor()
         snake_case :							List[str]           =			self.get_tokenizer()

         snake_case :							List[Any]           =			VisionTextDualEncoderProcessor(tokenizer=_UpperCamelCase							, image_processor=_UpperCamelCase      )

         snake_case :							Tuple           =			"""lower newer"""
         snake_case :							Union[str, Any]           =			self.prepare_image_inputs()

         snake_case :							List[str]           =			processor(text=_UpperCamelCase							, images=_UpperCamelCase      )

         self.assertListEqual(list(inputs.keys()      )							, processor.model_input_names      )







from math import sqrt






def   lowerCamelCase__							(							__lowerCAmelCase    :    int							):




			"""simple docstring"""


			lowerCAmelCase_         =      0
			for i in range(1       ,				int(sqrt(__lowerCAmelCase							) + 1							)							):
						if n % i == 0 and i != sqrt(__lowerCAmelCase							):
									total += i + n // i
						elif i == sqrt(__lowerCAmelCase							):
									total += i
			return total - n






def   lowerCamelCase__							(							__lowerCAmelCase    :    int = 10000							):




			"""simple docstring"""


			lowerCAmelCase_         =      sum(
			    i
			    for i in range(1       ,				__lowerCAmelCase							)
			    if sum_of_divisors(sum_of_divisors(__lowerCAmelCase							)							) == i and sum_of_divisors(__lowerCAmelCase							) != i							)
			return total


if __name__ == "__main__":
			print(solution(int(str(input()).strip())))



