


from ..utils import DummyObject, requires_backends



class   __lowerCamelCase (metaclass=_a	):
       _lowercase       							=    ["""torch""", """transformers""", """k_diffusion"""]







       def __init__( self:   Tuple,*A_:   Union[str, Any],**A_:   Tuple				):






            '''simple docstring'''




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







       @classmethod
       def 		snake_case_  ( cls:   int,*A_:   Optional[Any],**A_:   Tuple				):






            '''simple docstring'''




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







       @classmethod
       def 		snake_case_  ( cls:   int,*A_:   Tuple,**A_:   Tuple				):






            '''simple docstring'''




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






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 DeiTImageProcessor



class   __lowerCamelCase (unittest.TestCase	):







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






            '''simple docstring'''




            __UpperCamelCase   = size if size is not None else {'height': 20, 'width': 20}
            __UpperCamelCase   = crop_size if crop_size is not None else {'height': 18, 'width': 18}

            __UpperCamelCase   = parent
            __UpperCamelCase   = batch_size
            __UpperCamelCase   = num_channels
            __UpperCamelCase   = image_size
            __UpperCamelCase   = min_resolution
            __UpperCamelCase   = max_resolution
            __UpperCamelCase   = do_resize
            __UpperCamelCase   = size
            __UpperCamelCase   = do_center_crop
            __UpperCamelCase   = crop_size
            __UpperCamelCase   = do_normalize
            __UpperCamelCase   = image_mean
            __UpperCamelCase   = image_std







       def 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




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



@require_torch
@require_vision
class   __lowerCamelCase (_a							,			unittest.TestCase	):
       _lowercase       							=    DeiTImageProcessor if is_vision_available() else None
       _lowercase       							=    True







       def 		snake_case_  ( self:   List[Any]				):






            '''simple docstring'''




            __UpperCamelCase   = DeiTImageProcessingTester(self				)







       @property
       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()







       def 		snake_case_  ( self:   Union[str, Any]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            self.assertTrue(hasattr(A_,'do_resize'				)				)
            self.assertTrue(hasattr(A_,'size'				)				)
            self.assertTrue(hasattr(A_,'do_center_crop'				)				)
            self.assertTrue(hasattr(A_,'center_crop'				)				)
            self.assertTrue(hasattr(A_,'do_normalize'				)				)
            self.assertTrue(hasattr(A_,'image_mean'				)				)
            self.assertTrue(hasattr(A_,'image_std'				)				)







       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class.from_dict(self.image_processor_dict				)
            self.assertEqual(image_processor.size,{'height': 20, 'width': 20}				)
            self.assertEqual(image_processor.crop_size,{'height': 18, 'width': 18}				)

            __UpperCamelCase   = self.image_processing_class.from_dict(self.image_processor_dict,size=42,crop_size=84				)
            self.assertEqual(image_processor.size,{'height': 42, 'width': 42}				)
            self.assertEqual(image_processor.crop_size,{'height': 84, 'width': 84}				)







       def 		snake_case_  ( self:   Tuple				):






            '''simple docstring'''




            pass







       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




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

            # Test not batched input
            __UpperCamelCase   = 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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)







       def 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            # create random numpy tensors
            __UpperCamelCase   = 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
            __UpperCamelCase   = 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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)







       def 		snake_case_  ( self:   Any				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            # create random PyTorch tensors
            __UpperCamelCase   = 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
            __UpperCamelCase   = 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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,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.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)


