






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      :							Any       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							int=7       ,				_lowerCAmelCase      :							List[str]=3       ,				_lowerCAmelCase      :							Tuple=18       ,				_lowerCAmelCase      :							Optional[int]=30       ,				_lowerCAmelCase      :							Union[str, Any]=400       ,				_lowerCAmelCase      :							int=True       ,				_lowerCAmelCase      :							Tuple=32       ,				_lowerCAmelCase      :							List[str]=True       ,				)		-> Optional[int]:
					"""simple docstring"""







					__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  _a   (  self      :							List[str]			)		-> str:
					"""simple docstring"""







					return {
					    "do_resize": self.do_resize,
					    "size_divisor": self.size_divisor,
					    "do_rescale": self.do_rescale,
					}


@require_torch
@require_vision
class        __UpperCamelCase    (     UpperCAmelCase_   ,			unittest.TestCase   ):
	__snake_case	:Optional[Any]   =       GLPNImageProcessor if is_vision_available() else None







	def  _a   (  self      :							Tuple			)		-> Dict:
					"""simple docstring"""







					__lowercase             =  GLPNImageProcessingTester(self			)







	@property
	def  _a   (  self      :							str			)		-> Tuple:
					"""simple docstring"""







					return self.image_processor_tester.prepare_image_processor_dict()







	def  _a   (  self      :							Optional[int]			)		-> Optional[int]:
					"""simple docstring"""







					__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  _a   (  self      :							Tuple			)		-> Optional[int]:
					"""simple docstring"""







					pass







	def  _a   (  self      :							int			)		-> List[str]:
					"""simple docstring"""







					__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  _a   (  self      :							List[Any]			)		-> Union[str, Any]:
					"""simple docstring"""







					__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  _a   (  self      :							Any			)		-> Optional[Any]:
					"""simple docstring"""







					__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			)









from math import sqrt





def 				snake_case  (     lowerCamelCase					):




	'''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(sqrt(lowerCamelCase					) + 1					)				,  6					):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def 				snake_case  (     lowerCamelCase = 10_001					):




	'''simple docstring'''







	__lowercase             =  0
	__lowercase             =  1
	while count != nth and number < 3:
		number += 1
		if is_prime(lowerCamelCase					):
			count += 1
	while count != nth:
		number += 2
		if is_prime(lowerCamelCase					):
			count += 1
	return number


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

