






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        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


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





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     int:





     """simple docstring"""







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






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Optional[int]				      = GLPNImageProcessor if is_vision_available() else None


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						GLPNImageProcessingTester(self )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Any:





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Dict:





     """simple docstring"""







     lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Optional[int]:





     """simple docstring"""







     pass


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     List[str]:





     """simple docstring"""







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





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowercase__			    =						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)
     lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     lowercase__			    =						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)
     lowercase__			    =						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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     bool:
  if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise ValueError('Input series is not valid, valid series - [2, 4, 6]'	)
  if len(_SCREAMING_SNAKE_CASE	) == 0:
    raise ValueError('Input list must be a non empty list'	)
  if len(_SCREAMING_SNAKE_CASE	) == 1:
    return True
  lowercase__			    =						series[1] - series[0]
  for index in range(len(_SCREAMING_SNAKE_CASE	) - 1	):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     float:
  if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise ValueError('Input series is not valid, valid series - [2, 4, 6]'	)
  if len(_SCREAMING_SNAKE_CASE	) == 0:
    raise ValueError('Input list must be a non empty list'	)
  lowercase__			    =						0
  for val in series:
    answer += val
  return answer / len(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()


