





'''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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size
class        __lowerCAmelCase  (					unittest.TestCase				):






       """simple docstring"""





       def __init__(							self     :	str ,				lowerCAmelCase__     :	Dict ,				lowerCAmelCase__     :	Union[str, Any]=7 ,				lowerCAmelCase__     :	Dict=3 ,				lowerCAmelCase__     :	Optional[Any]=18 ,				lowerCAmelCase__     :	List[str]=30 ,				lowerCAmelCase__     :	Optional[Any]=400 ,				lowerCAmelCase__     :	List[Any]=True ,				lowerCAmelCase__     :	int=1 / 255 ,				lowerCAmelCase__     :	str=True ,				lowerCAmelCase__     :	int=8 ,				)					->  Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       					= parent
            _UpperCamelCase       					= batch_size
            _UpperCamelCase       					= num_channels
            _UpperCamelCase       					= image_size
            _UpperCamelCase       					= min_resolution
            _UpperCamelCase       					= max_resolution
            _UpperCamelCase       					= do_rescale
            _UpperCamelCase       					= rescale_factor
            _UpperCamelCase       					= do_pad
            _UpperCamelCase       					= pad_size





       def snake_case__		(							self     :	str  )					->  List[str]:

            '''simple docstring'''




            return {
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_pad": self.do_pad,
                "pad_size": self.pad_size,
            }







       def snake_case__		(							self     :	List[str] ,				lowerCAmelCase__     :	List[Any]=False ,				lowerCAmelCase__     :	Union[str, Any]=False ,				lowerCAmelCase__     :	Any=False  )					->  Optional[Any]:

            '''simple docstring'''




            assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

            if equal_resolution:
                 _UpperCamelCase       					= []
                 for i in range(self.batch_size  ):
                      image_inputs.append(
                          np.random.randint(
                              255 ,				size=(self.num_channels, self.max_resolution, self.max_resolution) ,				dtype=np.uinta  )  )
            else:
                 _UpperCamelCase       					= []
                 for i in range(self.batch_size  ):
                      _UpperCamelCase					,       _UpperCamelCase       					= np.random.choice(np.arange(self.min_resolution ,				self.max_resolution  ) ,				2  )
                      image_inputs.append(np.random.randint(255 ,				size=(self.num_channels, width, height) ,				dtype=np.uinta  )  )

            if not numpify and not torchify:
                 # PIL expects the channel dimension as last dimension
                 _UpperCamelCase       					= [Image.fromarray(np.moveaxis(lowerCAmelCase__ ,				0 ,				-1  )  ) for x in image_inputs]

            if torchify:
                 _UpperCamelCase       					= [torch.from_numpy(lowerCAmelCase__  ) for x in image_inputs]

            return image_inputs

@require_torch
@require_vision
class        __lowerCAmelCase  (					__magic_name__		,		unittest.TestCase				):






       """simple docstring"""
       _snake_case			:  Optional[Any]		       =					SwinaSRImageProcessor if is_vision_available() else None





       def snake_case__		(							self     :	int  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase       					= SwinaSRImageProcessingTester(self  )





       @property
       def snake_case__		(							self     :	Dict  )					->  Optional[int]:

            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()





       def snake_case__		(							self     :	str  )					->  Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       					= self.image_processing_class(**self.image_processor_dict  )
            self.assertTrue(hasattr(lowerCAmelCase__ ,				'''do_rescale'''  )  )
            self.assertTrue(hasattr(lowerCAmelCase__ ,				'''rescale_factor'''  )  )
            self.assertTrue(hasattr(lowerCAmelCase__ ,				'''do_pad'''  )  )
            self.assertTrue(hasattr(lowerCAmelCase__ ,				'''pad_size'''  )  )





       def snake_case__		(							self     :	int  )					->  Optional[int]:

            '''simple docstring'''




            pass





       def snake_case__		(							self     :	List[str] ,				lowerCAmelCase__     :	Optional[int]  )					->  str:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= get_image_size(lowerCAmelCase__  )
            _UpperCamelCase       					= self.image_processor_tester.pad_size

            _UpperCamelCase       					= (old_height // size + 1) * size - old_height
            _UpperCamelCase       					= (old_width // size + 1) * size - old_width
            return old_height + pad_height, old_width + pad_width





       def snake_case__		(							self     :	Optional[int]  )					->  Any:

            '''simple docstring'''




            _UpperCamelCase       					= self.image_processing_class(**self.image_processor_dict  )
            # create random PIL images
            _UpperCamelCase       					= self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__  )
            for image in image_inputs:
                 self.assertIsInstance(lowerCAmelCase__ ,				Image.Image  )

            # Test not batched input
            _UpperCamelCase       					= image_processor(image_inputs[0] ,				return_tensors='''pt'''  ).pixel_values
            _UpperCamelCase					,       _UpperCamelCase       					= self.calculate_expected_size(np.array(image_inputs[0]  )  )
            self.assertEqual(
                encoded_images.shape ,				(
                    1,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ) ,				)





       def snake_case__		(							self     :	List[Any]  )					->  str:

            '''simple docstring'''




            _UpperCamelCase       					= self.image_processing_class(**self.image_processor_dict  )
            # create random numpy tensors
            _UpperCamelCase       					= self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__ ,				numpify=lowerCAmelCase__  )
            for image in image_inputs:
                 self.assertIsInstance(lowerCAmelCase__ ,				np.ndarray  )

            # Test not batched input
            _UpperCamelCase       					= image_processor(image_inputs[0] ,				return_tensors='''pt'''  ).pixel_values
            _UpperCamelCase					,       _UpperCamelCase       					= self.calculate_expected_size(image_inputs[0]  )
            self.assertEqual(
                encoded_images.shape ,				(
                    1,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ) ,				)







       def snake_case__		(							self     :	int  )					->  Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       					= self.image_processing_class(**self.image_processor_dict  )
            # create random PyTorch tensors
            _UpperCamelCase       					= self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__ ,				torchify=lowerCAmelCase__  )
            for image in image_inputs:
                 self.assertIsInstance(lowerCAmelCase__ ,				torch.Tensor  )

            # Test not batched input
            _UpperCamelCase       					= image_processor(image_inputs[0] ,				return_tensors='''pt'''  ).pixel_values
            _UpperCamelCase					,       _UpperCamelCase       					= self.calculate_expected_size(image_inputs[0]  )
            self.assertEqual(
                encoded_images.shape ,				(
                    1,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ) ,				)










'''simple docstring'''

def       a__       (       lowercase      :  int   )	->	str:





     """simple docstring"""

     if not isinstance(lowercase,      lowercase   ):
          _UpperCamelCase       					= (
              '''operation can not be conducted on a object of type '''
              F"""{type(lowercase   ).__name__}"""
          )
          raise TypeError(lowercase   )
     _UpperCamelCase       					= ''''''
     for _ in range(0,      32   ):
          bit_string += str(number % 2   )
          _UpperCamelCase       					= number >> 1
     return bit_string







def       a__       (       lowercase      :  int   )	->	str:





     """simple docstring"""

     if number < 0:
          raise ValueError('''the value of input must be positive'''   )
     elif isinstance(lowercase,      lowercase   ):
          raise TypeError('''Input value must be a \'int\' type'''   )
     elif isinstance(lowercase,      lowercase   ):
          raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''   )
     _UpperCamelCase       					= 0
     # iterator over [1 to 32],since we are dealing with 32 bit integer
     for _ in range(1,      33   ):
          # left shift the bits by unity
          _UpperCamelCase       					= result << 1
          # get the end bit
          _UpperCamelCase       					= number % 2
          # right shift the bits by unity
          _UpperCamelCase       					= number >> 1
          # add that bit to our ans
          _UpperCamelCase       					= result | end_bit
     return get_reverse_bit_string(lowercase   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



