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 ViTImageProcessor




class 						__snake_case						(			unittest.TestCase		):





      '''simple docstring'''






      def __init__(       self  :					Any     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Optional[int]=7     ,	_UpperCamelCase  :					Optional[Any]=3     ,	_UpperCamelCase  :					Dict=18     ,	_UpperCamelCase  :					Tuple=30     ,	_UpperCamelCase  :					str=400     ,	_UpperCamelCase  :					List[Any]=True     ,	_UpperCamelCase  :					str=None     ,	_UpperCamelCase  :					List[str]=True     ,	_UpperCamelCase  :					List[str]=[0.5, 0.5, 0.5]     ,	_UpperCamelCase  :					str=[0.5, 0.5, 0.5]     ,	)					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    size if size is not None else {"""height""": 18, """width""": 18}
          _lowerCamelCase  :		int			    =    parent
          _lowerCamelCase  :		Optional[int]			    =    batch_size
          _lowerCamelCase  :		int			    =    num_channels
          _lowerCamelCase  :		Optional[int]			    =    image_size
          _lowerCamelCase  :		Optional[Any]			    =    min_resolution
          _lowerCamelCase  :		Dict			    =    max_resolution
          _lowerCamelCase  :		Optional[Any]			    =    do_resize
          _lowerCamelCase  :		int			    =    size
          _lowerCamelCase  :		List[Any]			    =    do_normalize
          _lowerCamelCase  :		Union[str, Any]			    =    image_mean
          _lowerCamelCase  :		Union[str, Any]			    =    image_std




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->List[str]:


          """simple docstring"""


          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }





@require_torch
@require_vision
class 						__snake_case						(			SCREAMING_SNAKE_CASE__			,	unittest.TestCase		):





      '''simple docstring'''






      _snake_case     		=							ViTImageProcessor if is_vision_available() else None



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


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    ViTImageProcessingTester(self)



      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->Any:


          """simple docstring"""


          return self.image_processor_tester.prepare_image_processor_dict()



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(_UpperCamelCase     ,	"""image_mean"""))
          self.assertTrue(hasattr(_UpperCamelCase     ,	"""image_std"""))
          self.assertTrue(hasattr(_UpperCamelCase     ,	"""do_normalize"""))
          self.assertTrue(hasattr(_UpperCamelCase     ,	"""do_resize"""))
          self.assertTrue(hasattr(_UpperCamelCase     ,	"""size"""))



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


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size     ,	{"""height""": 18, """width""": 18})

          _lowerCamelCase  :		Any			    =    self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42)
          self.assertEqual(image_processor.size     ,	{"""height""": 42, """width""": 42})



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


          """simple docstring"""


          pass



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


          """simple docstring"""


          _lowerCamelCase  :		str			    =    self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowerCamelCase  :		List[Any]			    =    prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=_UpperCamelCase)
          for image in image_inputs:
              self.assertIsInstance(_UpperCamelCase     ,	Image.Image)

          # Test not batched input
          _lowerCamelCase  :		List[Any]			    =    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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,	)

          # Test batched
          _lowerCamelCase  :		Dict			    =    image_processing(_UpperCamelCase     ,	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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,	)



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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowerCamelCase  :		int			    =    prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=_UpperCamelCase     ,	numpify=_UpperCamelCase)
          for image in image_inputs:
              self.assertIsInstance(_UpperCamelCase     ,	np.ndarray)

          # Test not batched input
          _lowerCamelCase  :		Dict			    =    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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,	)

          # Test batched
          _lowerCamelCase  :		Optional[Any]			    =    image_processing(_UpperCamelCase     ,	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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,	)




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


          """simple docstring"""


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

          # Test not batched input
          _lowerCamelCase  :		Optional[int]			    =    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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,	)

          # Test batched
          _lowerCamelCase  :		str			    =    image_processing(_UpperCamelCase     ,	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.size["""height"""],
                  self.image_processor_tester.size["""width"""],
              )     ,	)











from ..utils import DummyObject, requires_backends



class  SCREAMING_SNAKE_CASE_  ( metaclass=SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							["""torch""", """transformers""", """k_diffusion"""]





  def __init__(  self	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(self	,  ['''torch''', '''transformers''', '''k_diffusion''']						)





  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Dict:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)







  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)