






'''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 ViTImageProcessor


class 							_UpperCAmelCase    (		unittest.TestCase      ):




    def __init__(      self	:							List[str]	,  a	:							Dict	,  a	:							Any=1_3	,  a	:							List[str]=3	,  a	:							int=2_2_4	,  a	:							Optional[int]=3_0	,  a	:							Optional[int]=4_0_0	,  a	:							int=True	,  a	:							Optional[Any]=None	,  a	:							Optional[int]=True	,  a	:							str=[0.5, 0.5, 0.5]	,  a	:							List[Any]=[0.5, 0.5, 0.5]	,  ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  size if size is not None else {"height": 1_8, "width": 1_8}
       lowercase_						:     Any							     =  parent
       lowercase_						:     List[Any]							     =  batch_size
       lowercase_						:     Dict							     =  num_channels
       lowercase_						:     List[str]							     =  image_size
       lowercase_						:     Optional[int]							     =  min_resolution
       lowercase_						:     int							     =  max_resolution
       lowercase_						:     int							     =  do_resize
       lowercase_						:     Union[str, Any]							     =  size
       lowercase_						:     Any							     =  do_normalize
       lowercase_						:     List[Any]							     =  image_mean
       lowercase_						:     List[str]							     =  image_std





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }


@require_torch
@require_vision
class 							_UpperCAmelCase    (		UpperCAmelCase__					,       unittest.TestCase      ):
    __lowerCamelCase: Optional[Any]									=     ViTImageProcessor if is_vision_available() else None




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  EfficientFormerImageProcessorTester(self      )




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.image_proc_tester.prepare_image_processor_dict()




    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(lowerCamelCase__	,  "image_mean"      )      )
       self.assertTrue(hasattr(lowerCamelCase__	,  "image_std"      )      )
       self.assertTrue(hasattr(lowerCamelCase__	,  "do_normalize"      )      )
       self.assertTrue(hasattr(lowerCamelCase__	,  "do_resize"      )      )
       self.assertTrue(hasattr(lowerCamelCase__	,  "size"      )      )




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       pass




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       lowercase_						:     Optional[int]							     =  prepare_image_inputs(self.image_proc_tester	,  equal_resolution=lowerCamelCase__      )
       for image in image_inputs:
          self.assertIsInstance(lowerCamelCase__	,  Image.Image      )

       # Test not batched input
       lowercase_						:     int							     =  image_processor(image_inputs[0]	,  return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape	,  (
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,  )

       # Test batched
       lowercase_						:     str							     =  image_processor(lowerCamelCase__	,  return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape	,  (
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,  )




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     str							     =  self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       lowercase_						:     str							     =  prepare_image_inputs(self.image_proc_tester	,  equal_resolution=lowerCamelCase__	,  numpify=lowerCamelCase__      )
       for image in image_inputs:
          self.assertIsInstance(lowerCamelCase__	,  np.ndarray      )

       # Test not batched input
       lowercase_						:     int							     =  image_processor(image_inputs[0]	,  return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape	,  (
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,  )

       # Test batched
       lowercase_						:     Optional[int]							     =  image_processor(lowerCamelCase__	,  return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape	,  (
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,  )





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       lowercase_						:     List[Any]							     =  prepare_image_inputs(self.image_proc_tester	,  equal_resolution=lowerCamelCase__	,  torchify=lowerCamelCase__      )
       for image in image_inputs:
          self.assertIsInstance(lowerCamelCase__	,  torch.Tensor      )

       # Test not batched input
       lowercase_						:     Dict							     =  image_processor(image_inputs[0]	,  return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape	,  (
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,  )

       # Test batched
       lowercase_						:     str							     =  image_processor(lowerCamelCase__	,  return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape	,  (
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,  )





from math import isqrt
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							List[str]       =	[True] * max_number
   for i in range(2							,				isqrt(max_number - 1					) + 1					):
      if is_prime[i]:
         for j in range(i**2							,				lowercase							,				lowercase					):
            lowerCAmelCase_:							Dict       =	False

   return [i for i in range(2							,				lowercase					) if is_prime[i]]
def 		snake_case__	(		lowercase = 10**8					):
   lowerCAmelCase_:							Union[str, Any]       =	calculate_prime_numbers(max_number // 2					)

   lowerCAmelCase_:							Tuple       =	0
   lowerCAmelCase_:							List[Any]       =	0
   lowerCAmelCase_:							Dict       =	len(lowercase					) - 1
   while left <= right:
      while prime_numbers[left] * prime_numbers[right] >= max_number:
         right -= 1
      semiprimes_count += right - left + 1
      left += 1

   return semiprimes_count


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