

def 	a (     A__      :  int	)       -> bool:





      """simple docstring"""



      return sum(i for i in range(1 ,  number // 2 + 1	) if number % i == 0	) == number


if __name__ == "__main__":
  print('Program to check whether a number is a Perfect number or not...')
  lowercase_   							=  int(input('Enter number: ').strip())
  print(f"{number} is {'' if perfect(number) else 'not '}a Perfect Number.")





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			={'num examples': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            _lowercase      			=datasets.Features({'text': datasets.Value('string'	), 'numbers': datasets.Value('float32'	)}	)
            _lowercase      			=generate_example_dataset(
                os.path.join(A__ ,  'dataset.arrow'	) ,  A__ ,  num_examples=A__	)

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='numpy'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='pandas'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='torch' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='tensorflow' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(A__ ,  'wb'	) as f:
            f.write(json.dumps(A__	).encode('utf-8'	)	)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()


