



"""simple docstring"""

from __future__ import annotations

import math




def __lowercase    (							_a					):

    if num <= 0:
        snake_case_				:  int       =			f"{num}: Invalid input, please enter a positive integer."
        raise ValueError(_a					)

    snake_case_				:  Dict       =			[True] * (num + 1)
    snake_case_				:  List[Any]       =			[]
    snake_case_				:  Any       =			2
    snake_case_				:  Optional[Any]       =			int(math.sqrt(_a					)					)

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(_a					)

            # Set multiples of start be False
            for i in range(start * start	,    num + 1	,    _a					):
                if sieve[i] is True:
                    snake_case_				:  str       =			False

        start += 1

    for j in range(end + 1	,    num + 1					):
        if sieve[j] is True:
            prime.append(_a					)

    return prime


if __name__ == "__main__":
     print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))










"""simple docstring"""

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor




@require_torch
@require_vision
class  _UpperCAmelCase (    unittest.TestCase):

 @slow
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  List[Any]       =			AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     snake_case_				:  List[Any]       =			AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     model.to(lowercase_       )

     from datasets import load_dataset

     snake_case_				:  str       =			load_dataset('''nielsr/rvlcdip-demo'''       )

     snake_case_				:  Tuple       =			dataset['''train'''][0]['''image'''].convert('''RGB'''       )

     snake_case_				:  Union[str, Any]       =			image_processor(lowercase_						,  return_tensors='''pt'''       ).to(lowercase_       )

     # forward pass
     with torch.no_grad():
         snake_case_				:  List[Any]       =			model(**lowercase_       )
         snake_case_				:  int       =			outputs.logits

     snake_case_				:  Any       =			torch.Size((1, 16)       )
     self.assertEqual(logits.shape						,  lowercase_       )

     snake_case_				:  Any       =			torch.tensor(
         [-0.41_58, -0.40_92, -0.43_47]						,  device=lowercase_						,  dtype=torch.float						,  )
     self.assertTrue(torch.allclose(logits[0, :3]						,  lowercase_						,  atol=1E-4       )       )





