





'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor




class  UpperCAmelCase     (			unittest.TestCase   ):




      def __init__(			self			: Union[str, Any]			,			__snake_case			: Union[str, Any]			,			__snake_case			: List[str]=7			,			__snake_case			: Tuple=3			,			__snake_case			: int=18			,			__snake_case			: int=30			,			__snake_case			: Tuple=4_00			,			__snake_case			: Optional[Any]=True			,			__snake_case			: Any=None			,			__snake_case			: Any=True			,			__snake_case			: int=False			,			__snake_case			: Tuple=True			,			__snake_case			: Tuple=True			,			__snake_case			: int=[0.5, 0.5, 0.5]			,			__snake_case			: int=[0.5, 0.5, 0.5]			,			)				->      Optional[int]:
            _lowerCAmelCase							     =							parent
            _lowerCAmelCase							     =							batch_size
            _lowerCAmelCase							     =							num_channels
            _lowerCAmelCase							     =							image_size
            _lowerCAmelCase							     =							min_resolution
            _lowerCAmelCase							     =							max_resolution
            _lowerCAmelCase							     =							do_resize
            _lowerCAmelCase							     =							size if size is not None else {"""height""": 18, """width""": 20}
            _lowerCAmelCase							     =							do_thumbnail
            _lowerCAmelCase							     =							do_align_axis
            _lowerCAmelCase							     =							do_pad
            _lowerCAmelCase							     =							do_normalize
            _lowerCAmelCase							     =							image_mean
            _lowerCAmelCase							     =							image_std




      def 				lowercase__    (			self			: int  )				->      Union[str, Any]:
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_thumbnail": self.do_thumbnail,
                "do_align_long_axis": self.do_align_axis,
                "do_pad": self.do_pad,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }




@require_torch
@require_vision
class  UpperCAmelCase     (			snake_case_							,		unittest.TestCase   ):
      _lowercase:			Optional[Any]						     =   DonutImageProcessor if is_vision_available() else None




      def 				lowercase__    (			self			: Tuple  )				->      Dict:
            _lowerCAmelCase							     =							DonutImageProcessingTester(self  )




      @property
      def 				lowercase__    (			self			: Union[str, Any]  )				->      Optional[Any]:
            return self.image_processor_tester.prepare_image_processor_dict()




      def 				lowercase__    (			self			: Any  )				->      int:
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            self.assertTrue(hasattr(__snake_case			,			"""do_resize"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""size"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_thumbnail"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_align_long_axis"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_pad"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_normalize"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""image_mean"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""image_std"""  )  )




      def 				lowercase__    (			self			: Union[str, Any]  )				->      List[Any]:
            _lowerCAmelCase							     =							self.image_processing_class.from_dict(self.image_processor_dict  )
            self.assertEqual(image_processor.size			,			{"""height""": 18, """width""": 20}  )

            _lowerCAmelCase							     =							self.image_processing_class.from_dict(self.image_processor_dict			,			size=42  )
            self.assertEqual(image_processor.size			,			{"""height""": 42, """width""": 42}  )

            # Previous config had dimensions in (width, height) order
            _lowerCAmelCase							     =							self.image_processing_class.from_dict(self.image_processor_dict			,			size=(42, 84)  )
            self.assertEqual(image_processor.size			,			{"""height""": 84, """width""": 42}  )




      def 				lowercase__    (			self			: Union[str, Any]  )				->      List[Any]:
            pass




      @is_flaky()
      def 				lowercase__    (			self			: Tuple  )				->      List[str]:
            # Initialize image_processing
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            # create random PIL images
            _lowerCAmelCase							     =							prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case			,			Image.Image  )

            # Test not batched input
            _lowerCAmelCase							     =							image_processing(image_inputs[0]			,			return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape			,			(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )			,			)

            # Test batched
            _lowerCAmelCase							     =							image_processing(__snake_case			,			return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape			,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )			,			)




      @is_flaky()
      def 				lowercase__    (			self			: List[Any]  )				->      Dict:
            # Initialize image_processing
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            # create random numpy tensors
            _lowerCAmelCase							     =							prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__snake_case			,			numpify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case			,			np.ndarray  )

            # Test not batched input
            _lowerCAmelCase							     =							image_processing(image_inputs[0]			,			return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape			,			(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )			,			)

            # Test batched
            _lowerCAmelCase							     =							image_processing(__snake_case			,			return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape			,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )			,			)




      @is_flaky()
      def 				lowercase__    (			self			: Dict  )				->      str:
            # Initialize image_processing
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            # create random PyTorch tensors
            _lowerCAmelCase							     =							prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__snake_case			,			torchify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case			,			torch.Tensor  )

            # Test not batched input
            _lowerCAmelCase							     =							image_processing(image_inputs[0]			,			return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape			,			(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )			,			)

            # Test batched
            _lowerCAmelCase							     =							image_processing(__snake_case			,			return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape			,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""height"""],
                    self.image_processor_tester.size["""width"""],
                )			,			)








import itertools
import math




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




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					2
				while True:
								if is_prime(snake_case__					):
												yield num
								num += 1




def 				a						(							snake_case__:					int = 10_001					):


				'''simple docstring'''
				return next(itertools.islice(prime_generator()						,   nth - 1						,   snake_case__					)					)


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




