




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 GLPNImageProcessor







class   A(						unittest.TestCase       ):



 '''simple docstring'''


 def __init__(    self :  Union[str, Any]						,							A_ :  Optional[Any]						,							A_ :  Union[str, Any]=7						,							A_ :  Optional[int]=3						,							A_ :  Tuple=18						,							A_ :  Dict=30						,							A_ :  int=400						,							A_ :  int=True						,							A_ :  Tuple=32						,							A_ :  str=True						,							)       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   parent
       lowerCamelCase_							=   batch_size
       lowerCamelCase_							=   num_channels
       lowerCamelCase_							=   image_size
       lowerCamelCase_							=   min_resolution
       lowerCamelCase_							=   max_resolution
       lowerCamelCase_							=   do_resize
       lowerCamelCase_							=   size_divisor
       lowerCamelCase_							=   do_rescale





 def     a__			(    self :  Optional[Any]  )       ->     Optional[Any]:

       """simple docstring"""






       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }







@require_torch
@require_vision
class   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						GLPNImageProcessor if is_vision_available() else None

 def     a__			(    self :  Union[str, Any]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   GLPNImageProcessingTester(self  )

 @property
 def     a__			(    self :  List[str]  )       ->     Optional[Any]:

       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()

 def     a__			(    self :  Dict  )       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(A_						,							'do_resize'  )  )
       self.assertTrue(hasattr(A_						,							'size_divisor'  )  )
       self.assertTrue(hasattr(A_						,							'resample'  )  )
       self.assertTrue(hasattr(A_						,							'do_rescale'  )  )

 def     a__			(    self :  Union[str, Any]  )       ->     Optional[Any]:

       """simple docstring"""






       pass

 def     a__			(    self :  Any  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       lowerCamelCase_							=   prepare_image_inputs(self.image_processor_tester						,							equal_resolution=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							Image.Image  )

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       lowerCamelCase_							=   image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )

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

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       lowerCamelCase_							=   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 (GLPNImageProcessor doesn't support batching)
       lowerCamelCase_							=   image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )





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

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       lowerCamelCase_							=   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 (GLPNImageProcessor doesn't support batching)
       lowerCamelCase_							=   image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )









import math

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int					):


      '''simple docstring'''



      lowerCamelCase_							=   0
      lowerCamelCase_							=   0
      while num > 0:
            lowerCamelCase_							=   num % 8
            lowerCamelCase_							=   octal + (remainder * math.floor(math.pow(10     ,							lowercase					)					))
            counter += 1
            lowerCamelCase_							=   math.floor(num / 8					)  # basically /= 8 without remainder if any
            # This formatting removes trailing '.0' from `octal`.
      return f"""0o{int(lowercase					)}"""

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      print('\n2 in octal is:'					)
      print(decimal_to_octal(2					)					)  # = 2
      print('\n8 in octal is:'					)
      print(decimal_to_octal(8					)					)  # = 10
      print('\n65 in octal is:'					)
      print(decimal_to_octal(65					)					)  # = 101
      print('\n216 in octal is:'					)
      print(decimal_to_octal(2_16					)					)  # = 330
      print('\n512 in octal is:'					)
      print(decimal_to_octal(5_12					)					)  # = 1000
      print('\n'					)


if __name__ == "__main__":
 main()



