"""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 PoolFormerImageProcessor







class  UpperCamelCase	(    unittest.TestCase						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase=7  ,__UpperCamelCase=3  ,__UpperCamelCase=30  ,__UpperCamelCase=400  ,__UpperCamelCase=True  ,__UpperCamelCase=None  ,__UpperCamelCase=0.9  ,__UpperCamelCase=None  ,__UpperCamelCase=True  ,__UpperCamelCase=[0.5, 0.5, 0.5]  ,__UpperCamelCase=[0.5, 0.5, 0.5]  ,)			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			size if size is not None else {'shortest_edge': 30}
        lowercase_			:       Tuple									=			crop_size if crop_size is not None else {'height': 30, 'width': 30}
        lowercase_			:       Any									=			parent
        lowercase_			:       Optional[Any]									=			batch_size
        lowercase_			:       Union[str, Any]									=			num_channels
        lowercase_			:       List[Any]									=			min_resolution
        lowercase_			:       Tuple									=			max_resolution
        lowercase_			:       Any									=			do_resize_and_center_crop
        lowercase_			:       Optional[int]									=			size
        lowercase_			:       List[str]									=			crop_pct
        lowercase_			:       str									=			crop_size
        lowercase_			:       List[Any]									=			do_normalize
        lowercase_			:       Optional[Any]									=			image_mean
        lowercase_			:       int									=			image_std







   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return {
            "size": self.size,
            "do_resize_and_center_crop": self.do_resize_and_center_crop,
            "crop_pct": self.crop_pct,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }







@require_torch
@require_vision
class  UpperCamelCase	(    lowercase_		,   unittest.TestCase						):
   lowercase							=						PoolFormerImageProcessor if is_vision_available() else None
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        lowercase_			:       int									=			PoolFormerImageProcessingTester(self    )
   @property
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        return self.image_processor_tester.prepare_image_processor_dict()
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(__UpperCamelCase  ,'do_resize_and_center_crop'    )    )
        self.assertTrue(hasattr(__UpperCamelCase  ,'size'    )    )
        self.assertTrue(hasattr(__UpperCamelCase  ,'crop_pct'    )    )
        self.assertTrue(hasattr(__UpperCamelCase  ,'do_normalize'    )    )
        self.assertTrue(hasattr(__UpperCamelCase  ,'image_mean'    )    )
        self.assertTrue(hasattr(__UpperCamelCase  ,'image_std'    )    )
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			self.image_processing_class.from_dict(self.image_processor_dict    )
        self.assertEqual(image_processor.size  ,{'shortest_edge': 30}    )
        self.assertEqual(image_processor.crop_size  ,{'height': 30, 'width': 30}    )

        lowercase_			:       Union[str, Any]									=			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     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        pass
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       str									=			self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        lowercase_			:       Union[str, Any]									=			prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCamelCase    )
        for image in image_inputs:
             self.assertIsInstance(__UpperCamelCase  ,Image.Image    )

        # Test not batched input
        lowercase_			:       Dict									=			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
        lowercase_			:       Union[str, Any]									=			image_processing(__UpperCamelCase  ,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     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       int									=			self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        lowercase_			:       Tuple									=			prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCamelCase  ,numpify=__UpperCamelCase    )
        for image in image_inputs:
             self.assertIsInstance(__UpperCamelCase  ,np.ndarray    )

        # Test not batched input
        lowercase_			:       Tuple									=			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
        lowercase_			:       Dict									=			image_processing(__UpperCamelCase  ,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     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       int									=			self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        lowercase_			:       Dict									=			prepare_image_inputs(self.image_processor_tester  ,equal_resolution=__UpperCamelCase  ,torchify=__UpperCamelCase    )
        for image in image_inputs:
             self.assertIsInstance(__UpperCamelCase  ,torch.Tensor    )

        # Test not batched input
        lowercase_			:       Dict									=			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
        lowercase_			:       Union[str, Any]									=			image_processing(__UpperCamelCase  ,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'],
            )  ,)


"""simple docstring"""







from math import sqrt



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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int = 1_00_01   ):
     lowercase_			:       str									=			0
     lowercase_			:       Optional[Any]									=			1
     while count != nth and number < 3:
          number += 1
          if is_prime(__SCREAMING_SNAKE_CASE   ):
               count += 1
     while count != nth:
          number += 2
          if is_prime(__SCREAMING_SNAKE_CASE   ):
               count += 1
     return number


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

