




"""simple docstring"""




import math
from collections.abc import Iterator
from itertools import takewhile
def        A_      (		snake_case_  :							int     ):







    '''simple docstring'''

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5    ,int(math.sqrt(_UpperCAmelCase     ) + 1     )    ,6     ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True
def        A_      (		):







    '''simple docstring'''
    UpperCamelCase					: int 							=				2
    while True:
        if is_prime(_UpperCAmelCase     ):
            yield num
        num += 1
def        A_      (		snake_case_  :							int = 2_0_0_0_0_0_0     ):







    '''simple docstring'''
    return sum(takewhile(lambda snake_case_     : x < n    ,prime_generator()     )     )


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











"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class       a							(   a__   , a__   , unittest.TestCase  ):
 snake_case__             =       IFInpaintingPipeline
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 snake_case__             =       PipelineTesterMixin.required_optional_params - {'''latents'''}



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self._get_dummy_components()



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=0     ):







  """simple docstring"""






  if str(_snake_case     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(_snake_case     )
  else:
   lowerCAmelCase    				= torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )

  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )
  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )

  lowerCAmelCase    				= {
      'prompt': 'A painting of a squirrel eating a burger',
      'image': image,
      'mask_image': mask_image,
      'generator': generator,
      'num_inference_steps': 2,
      'output_type': 'numpy',
  }

  return inputs



 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()				,       reason='XFormers attention is only available with CUDA and `xformers` installed'				,       )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != 'cuda'				,       reason='float16 requires CUDA'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  super().test_save_load_floataa(expected_max_diff=1E-1     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_local()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_inference_batch_single_identical(
      expected_max_diff=1E-2				,       )





