





'''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 OwlViTImageProcessor

class 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 def __init__(	self	:							List[Any]							,     __A	:							Optional[Any]							,     __A	:							List[str]=7							,     __A	:							List[str]=3							,     __A	:							str=1_8							,     __A	:							int=3_0							,     __A	:							str=4_0_0							,     __A	:							List[str]=True							,     __A	:							List[Any]=None							,     __A	:							Optional[Any]=True							,     __A	:							Any=None							,     __A	:							str=True							,     __A	:							List[str]=[0.4814_5466, 0.457_8275, 0.4082_1073]							,     __A	:							Optional[Any]=[0.2686_2954, 0.2613_0258, 0.2757_7711]							,     __A	:							int=True							,     ):
        __UpperCamelCase	=  parent
        __UpperCamelCase	=  batch_size
        __UpperCamelCase	=  num_channels
        __UpperCamelCase	=  image_size
        __UpperCamelCase	=  min_resolution
        __UpperCamelCase	=  max_resolution
        __UpperCamelCase	=  do_resize
        __UpperCamelCase	=  size if size is not None else {'height': 1_8, 'width': 1_8}
        __UpperCamelCase	=  do_center_crop
        __UpperCamelCase	=  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
        __UpperCamelCase	=  do_normalize
        __UpperCamelCase	=  image_mean
        __UpperCamelCase	=  image_std
        __UpperCamelCase	=  do_convert_rgb






 def 	_lowerCamelCase (	self	:							Dict     ):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }






@require_torch
@require_vision
class 					snake_case					(   __lowerCamelCase      ,	unittest.TestCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : List[Any]      		=OwlViTImageProcessor if is_vision_available() else None






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	=  OwlViTImageProcessingTester(self     )






 @property
 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        return self.image_processor_tester.prepare_image_processor_dict()






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(__A							,     'do_resize'     )     )
        self.assertTrue(hasattr(__A							,     'size'     )     )
        self.assertTrue(hasattr(__A							,     'do_center_crop'     )     )
        self.assertTrue(hasattr(__A							,     'center_crop'     )     )
        self.assertTrue(hasattr(__A							,     'do_normalize'     )     )
        self.assertTrue(hasattr(__A							,     'image_mean'     )     )
        self.assertTrue(hasattr(__A							,     'image_std'     )     )
        self.assertTrue(hasattr(__A							,     'do_convert_rgb'     )     )






 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	=  self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size							,     {'height': 1_8, 'width': 1_8}     )
        self.assertEqual(image_processor.crop_size							,     {'height': 1_8, 'width': 1_8}     )

        __UpperCamelCase	=  self.image_processing_class.from_dict(self.image_processor_dict							,     size=4_2							,     crop_size=8_4     )
        self.assertEqual(image_processor.size							,     {'height': 4_2, 'width': 4_2}     )
        self.assertEqual(image_processor.crop_size							,     {'height': 8_4, 'width': 8_4}     )






 def 	_lowerCamelCase (	self	:							int     ):
        # Initialize image_processing
        __UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        __UpperCamelCase	=  prepare_image_inputs(self.image_processor_tester							,     equal_resolution=__A     )

        for image in image_inputs:
               self.assertIsInstance(__A							,     Image.Image     )

        # Test not batched input
        __UpperCamelCase	=  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
        __UpperCamelCase	=  image_processing(__A							,     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	:							Union[str, Any]     ):
        # Initialize image_processing
        __UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        __UpperCamelCase	=  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
        __UpperCamelCase	=  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
        __UpperCamelCase	=  image_processing(__A							,     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]     ):
        # Initialize image_processing
        __UpperCamelCase	=  self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        __UpperCamelCase	=  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
        __UpperCamelCase	=  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
        __UpperCamelCase	=  image_processing(__A							,     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'''
def     lowercase__       (       __lowercase						:  int = 20					)			->				int:



       """simple docstring"""






       try:
              __UpperCamelCase	=  int(__lowercase					)
       except (TypeError, ValueError):
              raise TypeError('Parameter n must be int or castable to int.'					)
       if n <= 0:
              raise ValueError('Parameter n must be greater than or equal to one.'					)
       __UpperCamelCase	=  0
       while 1:
              i += n * (n - 1)
              __UpperCamelCase	=  0
              for j in range(2    ,  __lowercase					):
                     if i % j != 0:
                            __UpperCamelCase	=  1
                            break
              if nfound == 0:
                     if i == 0:
                            __UpperCamelCase	=  1
                     return i
       return None


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