






"""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 					_UpperCAmelCase       ( unittest.TestCase							):




					def __init__(				self     :						Dict    ,						A     :						List[Any]    ,						A     :						Tuple=7    ,						A     :						Dict=3    ,						A     :						Optional[Any]=18    ,						A     :						Union[str, Any]=30    ,						A     :						List[str]=4_00    ,						A     :						List[Any]=True    ,						A     :						Union[str, Any]=32    ,						A     :						Any=True    ,						)  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   parent
							lowercase_ : Optional[int]							       =   batch_size
							lowercase_ : Any							       =   num_channels
							lowercase_ : List[str]							       =   image_size
							lowercase_ : Optional[int]							       =   min_resolution
							lowercase_ : Dict							       =   max_resolution
							lowercase_ : str							       =   do_resize
							lowercase_ : Optional[Any]							       =   size_divisor
							lowercase_ : List[Any]							       =   do_rescale






					def   A  (				self     :						Dict   )  ->  Optional[int]:
							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}





@require_torch
@require_vision
class 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   GLPNImageProcessor if is_vision_available() else None




					def   A  (				self     :						Union[str, Any]   )  ->  List[str]:
							lowercase_ : Any							       =   GLPNImageProcessingTester(self   )




					@property
					def   A  (				self     :						int   )  ->  Optional[int]:
							return self.image_processor_tester.prepare_image_processor_dict()




					def   A  (				self     :						List[str]   )  ->  int:
							lowercase_ : Any							       =   self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(A    ,						'''do_resize'''   )   )
							self.assertTrue(hasattr(A    ,						'''size_divisor'''   )   )
							self.assertTrue(hasattr(A    ,						'''resample'''   )   )
							self.assertTrue(hasattr(A    ,						'''do_rescale'''   )   )




					def   A  (				self     :						Any   )  ->  str:
							pass




					def   A  (				self     :						Tuple   )  ->  Tuple:
							# Initialize image_processing
							lowercase_ : List[Any]							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							lowercase_ : Optional[int]							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						Image.Image   )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowercase_ : str							       =   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   A  (				self     :						List[Any]   )  ->  Tuple:
							# Initialize image_processing
							lowercase_ : Union[str, Any]							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							lowercase_ : str							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A    ,						numpify=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						np.ndarray   )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowercase_ : Any							       =   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   A  (				self     :						Optional[int]   )  ->  List[Any]:
							# Initialize image_processing
							lowercase_ : Optional[int]							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							lowercase_ : Tuple							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A    ,						torchify=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						torch.Tensor   )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowercase_ : Optional[int]							       =   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"""
from math import isqrt, loga






def 					A__				(			UpperCamelCase       ):
							A		=  [True] * max_number
							for i in range(2					,  isqrt(max_number - 1       ) + 1       ):
														if is_prime[i]:
																					for j in range(i**2					,  UpperCamelCase					,  UpperCamelCase       ):
																												A		=  False

							return [i for i in range(2					,  UpperCamelCase       ) if is_prime[i]]






def 					A__				(			UpperCamelCase = 800_800					,  UpperCamelCase = 800_800       ):
							A		=  degree * loga(UpperCamelCase       )
							A		=  int(UpperCamelCase       )
							A		=  calculate_prime_numbers(UpperCamelCase       )

							A		=  0
							A		=  0
							A		=  len(UpperCamelCase       ) - 1
							while left < right:
														while (
														    prime_numbers[right] * loga(prime_numbers[left]       )
														    + prime_numbers[left] * loga(prime_numbers[right]       )
														    > upper_bound
														):
																					right -= 1
														hybrid_integers_count += right - left
														left += 1

							return hybrid_integers_count


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


