

'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor






class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Union[str, Any]=7      ,lowercase__     :  Optional[Any]=3      ,lowercase__     :  Optional[int]=1_8      ,lowercase__     :  str=3_0      ,lowercase__     :  Any=4_0_0      ,lowercase__     :  List[Any]=True      ,lowercase__     :  Union[str, Any]=3_2      ,lowercase__     :  int=True      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				num_channels
   __lowercase													=				image_size
   __lowercase													=				min_resolution
   __lowercase													=				max_resolution
   __lowercase													=				do_resize
   __lowercase													=				size_divisor
   __lowercase													=				do_rescale






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   return {
       "do_resize": self.do_resize,
       "size_divisor": self.size_divisor,
       "do_rescale": self.do_rescale,
   }






@require_torch
@require_vision
class     lowercase_			(lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    GLPNImageProcessor if is_vision_available() else None






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				GLPNImageProcessingTester(self    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   return self.image_processor_tester.prepare_image_processor_dict()






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				self.image_processing_class(**self.image_processor_dict    )
   self.assertTrue(hasattr(lowercase__      ,'''do_resize'''    )    )
   self.assertTrue(hasattr(lowercase__      ,'''size_divisor'''    )    )
   self.assertTrue(hasattr(lowercase__      ,'''resample'''    )    )
   self.assertTrue(hasattr(lowercase__      ,'''do_rescale'''    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   pass






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   # Initialize image_processing
   __lowercase													=				self.image_processing_class(**self.image_processor_dict    )
   # create random PIL images
   __lowercase													=				prepare_image_inputs(self.image_processor_tester      ,equal_resolution=lowercase__    )
   for image in image_inputs:
    self.assertIsInstance(lowercase__      ,Image.Image    )

   # Test not batched input (GLPNImageProcessor doesn't support batching)
   __lowercase													=				image_processing(image_inputs[0]      ,return_tensors='''pt'''    ).pixel_values
   self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
   self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   # Initialize image_processing
   __lowercase													=				self.image_processing_class(**self.image_processor_dict    )
   # create random numpy tensors
   __lowercase													=				prepare_image_inputs(self.image_processor_tester      ,equal_resolution=lowercase__      ,numpify=lowercase__    )
   for image in image_inputs:
    self.assertIsInstance(lowercase__      ,np.ndarray    )

   # Test not batched input (GLPNImageProcessor doesn't support batching)
   __lowercase													=				image_processing(image_inputs[0]      ,return_tensors='''pt'''    ).pixel_values
   self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
   self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   # Initialize image_processing
   __lowercase													=				self.image_processing_class(**self.image_processor_dict    )
   # create random PyTorch tensors
   __lowercase													=				prepare_image_inputs(self.image_processor_tester      ,equal_resolution=lowercase__      ,torchify=lowercase__    )
   for image in image_inputs:
    self.assertIsInstance(lowercase__      ,torch.Tensor    )

   # Test not batched input (GLPNImageProcessor doesn't support batching)
   __lowercase													=				image_processing(image_inputs[0]      ,return_tensors='''pt'''    ).pixel_values
   self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
   self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )




'''simple docstring'''






def 	_A  (					A__ = 1000000   ):






 """simple docstring"""



 __lowercase													=				set(range(3  ,       A__  ,       2   )   )
 primes.add(2   )
 for p in range(3  ,       A__  ,       2   ):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p  ,       A__  ,       A__   )   )   )

 __lowercase													=				[float(A__   ) for n in range(limit + 1   )]

 for p in primes:
  for n in range(A__  ,       limit + 1  ,       A__   ):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]   )   )


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

