
"""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 GLPNImageProcessor



class 		UpperCAmelCase_						(unittest.TestCase    ):
 """simple docstring"""




 def __init__(			self							:			str  , a_							:			Union[str, Any]  , a_							:			Tuple=7  , a_							:			Tuple=3  , a_							:			str=18  , a_							:			Union[str, Any]=30  , a_							:			List[str]=4_00  , a_							:			str=True  , a_							:			List[Any]=32  , a_							:			int=True  , )->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	parent
      UpperCAmelCase_    :     Union[str, Any]            =	batch_size
      UpperCAmelCase_    :     List[str]            =	num_channels
      UpperCAmelCase_    :     Optional[Any]            =	image_size
      UpperCAmelCase_    :     Dict            =	min_resolution
      UpperCAmelCase_    :     Optional[int]            =	max_resolution
      UpperCAmelCase_    :     str            =	do_resize
      UpperCAmelCase_    :     List[Any]            =	size_divisor
      UpperCAmelCase_    :     List[str]            =	do_rescale






 def 	a   (			self							:			str			)->       Tuple:



      """simple docstring"""

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



@require_torch
@require_vision
class 		UpperCAmelCase_						(lowerCamelCase_	,			unittest.TestCase    ):
 """simple docstring"""




 UpperCamelCase_    :  Tuple					 =     GLPNImageProcessor if is_vision_available() else None




 def 	a   (			self							:			Optional[int]			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     str            =	GLPNImageProcessingTester(self			)




 @property
 def 	a   (			self							:			int			)->       Optional[Any]:



      """simple docstring"""

      return self.image_processor_tester.prepare_image_processor_dict()




 def 	a   (			self							:			Any			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[Any]            =	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							:			Optional[Any]			)->       Tuple:



      """simple docstring"""

      pass




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



      """simple docstring"""

      UpperCAmelCase_    :     Any            =	self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      UpperCAmelCase_    :     str            =	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)
      UpperCAmelCase_    :     List[str]            =	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							:			Union[str, Any]			)->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Dict            =	self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      UpperCAmelCase_    :     Optional[Any]            =	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)
      UpperCAmelCase_    :     Dict            =	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							:			Dict			)->       Dict:



      """simple docstring"""

      UpperCAmelCase_    :     Dict            =	self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      UpperCAmelCase_    :     Tuple            =	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)
      UpperCAmelCase_    :     Optional[int]            =	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			)



"""simple docstring"""







def       A_      (       lowercase    ,			lowercase   )  ->				int:


     """simple docstring"""







     return number | (1 << position)




def       A_      (       lowercase    ,			lowercase   )  ->				int:


     """simple docstring"""







     return number & ~(1 << position)




def       A_      (       lowercase    ,			lowercase   )  ->				int:


     """simple docstring"""







     return number ^ (1 << position)




def       A_      (       lowercase    ,			lowercase   )  ->				bool:


     """simple docstring"""







     return ((number >> position) & 1) == 1




def       A_      (       lowercase    ,			lowercase   )  ->				int:


     """simple docstring"""







     return int((number & (1 << position)) != 0   )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

