


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 MobileViTImageProcessor







class  snake_case_     (unittest.TestCase   ):
     def __init__(   self     :Dict    ,__snake_case     :int    ,__snake_case     :Optional[int]=7    ,__snake_case     :List[Any]=3    ,__snake_case     :Dict=18    ,__snake_case     :Dict=30    ,__snake_case     :Optional[int]=4_00    ,__snake_case     :int=True    ,__snake_case     :str=None    ,__snake_case     :Dict=True    ,__snake_case     :Tuple=None    ,__snake_case     :List[str]=True    ,)      ->					List[str]:
            a__    =  size if size is not None else {"shortest_edge": 20}
            a__    =  crop_size if crop_size is not None else {"height": 18, "width": 18}
            a__    =  parent
            a__    =  batch_size
            a__    =  num_channels
            a__    =  image_size
            a__    =  min_resolution
            a__    =  max_resolution
            a__    =  do_resize
            a__    =  size
            a__    =  do_center_crop
            a__    =  crop_size
            a__    =  do_flip_channel_order



     def 				lowerCamelCase__(   self     :List[Any]   )      ->					Any:
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_flip_channel_order": self.do_flip_channel_order,
            }







@require_torch
@require_vision
class  snake_case_     (_UpperCamelCase			,		unittest.TestCase   ):
     UpperCAmelCase__      :					Tuple					   =    MobileViTImageProcessor if is_vision_available() else None
     def 				lowerCamelCase__(   self     :str   )      ->					List[str]:
            a__    =  MobileViTImageProcessingTester(self   )
     @property
     def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					str:
            return self.image_processor_tester.prepare_image_processor_dict()
     def 				lowerCamelCase__(   self     :Optional[int]   )      ->					Optional[int]:
            a__    =  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_center_crop'   )   )
            self.assertTrue(hasattr(__snake_case    ,'center_crop'   )   )
            self.assertTrue(hasattr(__snake_case    ,'do_flip_channel_order'   )   )
     def 				lowerCamelCase__(   self     :List[Any]   )      ->					Dict:
            a__    =  self.image_processing_class.from_dict(self.image_processor_dict   )
            self.assertEqual(image_processor.size    ,{'shortest_edge': 20}   )
            self.assertEqual(image_processor.crop_size    ,{'height': 18, 'width': 18}   )

            a__    =  self.image_processing_class.from_dict(self.image_processor_dict    ,size=42    ,crop_size=84   )
            self.assertEqual(image_processor.size    ,{'shortest_edge': 42}   )
            self.assertEqual(image_processor.crop_size    ,{'height': 84, 'width': 84}   )
     def 				lowerCamelCase__(   self     :Tuple   )      ->					int:
            pass
     def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Tuple:
            # Initialize image_processing
            a__    =  self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            a__    =  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
            a__    =  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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )    ,)

            # Test batched
            a__    =  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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )    ,)
     def 				lowerCamelCase__(   self     :List[Any]   )      ->					Any:
            # Initialize image_processing
            a__    =  self.image_processing_class(**self.image_processor_dict   )
            # create random numpy tensors
            a__    =  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
            a__    =  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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )    ,)

            # Test batched
            a__    =  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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )    ,)



     def 				lowerCamelCase__(   self     :List[str]   )      ->					Tuple:
            # Initialize image_processing
            a__    =  self.image_processing_class(**self.image_processor_dict   )
            # create random PyTorch tensors
            a__    =  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
            a__    =  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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )    ,)

            # Test batched
            a__    =  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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )    ,)








from math import sqrt






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							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 							__lowercase     (				__lowerCAmelCase							:       int = 1_0_0_0_1     ):
							a__    =  0
							a__    =  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() = }""")




