






def 						_a    (      a :int = 1_000 )							->			int:
 a		  =      2**power
 a		  =      str(a )
 a		  =      list(a )
 a		  =      0

 for i in list_num:
  sum_of_num += int(a )

 return sum_of_num


if __name__ == "__main__":
     UpperCAmelCase__				   =		int(input("Enter the power of 2: ").strip())
     print("2 ^ ", power, " = ", 2**power)
     UpperCAmelCase__				   =		solution(power)
     print("Sum of the digits is: ", result)










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       __lowerCAmelCase					(      self      :						List[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      tempfile.mkdtemp()

       # fmt: off
       a		  =      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
       # fmt: on
       a		  =      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]  )  )

       a		  =      {
           '''do_resize''': True,
           '''size''': {'''height''': 18, '''width''': 18},
           '''do_normalize''': True,
           '''image_mean''': [0.5, 0.5, 0.5],
           '''image_std''': [0.5, 0.5, 0.5],
       }
       a		  =      os.path.join(self.tmpdirname ,					__UpperCAmelCase  )
       with open(self.image_processor_file ,					'''w''' ,					encoding='''utf-8'''  ) as fp:
        json.dump(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						List[Any] ,					**__UpperCAmelCase      :						List[Any]  )    ->int:




       """simple docstring"""


       return BertTokenizer.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any] ,					**__UpperCAmelCase      :						Optional[int]  )    ->Union[str, Any]:




       """simple docstring"""


       return ViTImageProcessor.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


       shutil.rmtree(self.tmpdirname  )







      def       __lowerCAmelCase					(      self      :						str  )    ->Dict:




       """simple docstring"""


       a		  =      [np.random.randint(255 ,					size=(3, 30, 400) ,					dtype=np.uinta  )]

       a		  =      [Image.fromarray(np.moveaxis(__UpperCAmelCase ,					0 ,					-1  )  ) for x in image_inputs]

       return image_inputs







      def       __lowerCAmelCase					(      self      :						Any  )    ->Tuple:




       """simple docstring"""


       a		  =      self.get_tokenizer()
       a		  =      self.get_image_processor()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       processor.save_pretrained(self.tmpdirname  )
       a		  =      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       __lowerCAmelCase					(      self      :						int  )    ->Dict:




       """simple docstring"""


       a		  =      VisionTextDualEncoderProcessor(
           tokenizer=self.get_tokenizer() ,					image_processor=self.get_image_processor()  )
       processor.save_pretrained(self.tmpdirname  )

       a		  =      self.get_tokenizer(bos_token='''(BOS)''' ,					eos_token='''(EOS)'''  )
       a		  =      self.get_image_processor(do_normalize=__UpperCAmelCase ,					padding_value=1.0  )

       a		  =      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       __lowerCAmelCase					(      self      :						Tuple  )    ->Dict:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      self.prepare_image_inputs()

       a		  =      image_processor(__UpperCAmelCase ,					return_tensors='''np'''  )
       a		  =      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       __lowerCAmelCase					(      self      :						List[str]  )    ->str:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''lower newer'''

       a		  =      processor(text=__UpperCAmelCase  )

       a		  =      tokenizer(__UpperCAmelCase  )

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







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''lower newer'''
       a		  =      self.prepare_image_inputs()

       a		  =      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       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       a		  =      processor.batch_decode(__UpperCAmelCase  )
       a		  =      tokenizer.batch_decode(__UpperCAmelCase  )

       self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Dict:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      VisionTextDualEncoderProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''lower newer'''
       a		  =      self.prepare_image_inputs()

       a		  =      processor(text=__UpperCAmelCase ,					images=__UpperCAmelCase  )

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


