




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
    import numpy as np
    import torch

if is_vision_available():
    from PIL import Image





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      Dict=False   ,       UpperCAmelCase_	:      Tuple=False   ,       UpperCAmelCase_	:      str=False      )->			Tuple:

       """simple docstring"""




       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       a			   =[]
       for i in range(image_processor_tester.batch_size      ):
              if equal_resolution:
                     a			   =a			   =image_processor_tester.max_resolution
              else:
                     # To avoid getting image width/height 0
                     a			   =image_processor_tester.min_resolution
                     if getattr(UpperCAmelCase_   ,       """size_divisor"""   ,       UpperCAmelCase_      ):
                            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                            a			   =max(image_processor_tester.size_divisor   ,       UpperCAmelCase_      )
                     a   ,      a			   =np.random.choice(np.arange(UpperCAmelCase_   ,       image_processor_tester.max_resolution      )   ,       2      )
              image_inputs.append(
                  np.random.randint(255   ,       size=(image_processor_tester.num_channels, width, height)   ,       dtype=np.uinta      )      )

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              a			   =[Image.fromarray(np.moveaxis(UpperCAmelCase_   ,       0   ,       -1      )      ) for image in image_inputs]

       if torchify:
              a			   =[torch.from_numpy(UpperCAmelCase_      ) for image in image_inputs]

       return image_inputs





def 							lowerCamelCase    (							UpperCAmelCase_	:      Optional[int]   ,       UpperCAmelCase_	:      List[str]=10   ,       UpperCAmelCase_	:      int=10   ,       UpperCAmelCase_	:      Any=False   ,       UpperCAmelCase_	:      Union[str, Any]=False      )->			Optional[Any]:

       """simple docstring"""




       a			   =[]
       for i in range(image_processor_tester.num_frames      ):
              video.append(np.random.randint(255   ,       size=(image_processor_tester.num_channels, width, height)   ,       dtype=np.uinta      )      )

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              a			   =[Image.fromarray(np.moveaxis(UpperCAmelCase_   ,       0   ,       -1      )      ) for frame in video]

       if torchify:
              a			   =[torch.from_numpy(UpperCAmelCase_      ) for frame in video]

       return video





def 							lowerCamelCase    (							UpperCAmelCase_	:      int   ,       UpperCAmelCase_	:      str=False   ,       UpperCAmelCase_	:      str=False   ,       UpperCAmelCase_	:      Optional[int]=False      )->			Any:

       """simple docstring"""




       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       a			   =[]
       for i in range(image_processor_tester.batch_size      ):
              if equal_resolution:
                     a			   =a			   =image_processor_tester.max_resolution
              else:
                     a   ,      a			   =np.random.choice(
                         np.arange(image_processor_tester.min_resolution   ,       image_processor_tester.max_resolution      )   ,       2      )
                     a			   =prepare_video(
                         image_processor_tester=UpperCAmelCase_   ,       width=UpperCAmelCase_   ,       height=UpperCAmelCase_   ,       numpify=UpperCAmelCase_   ,       torchify=UpperCAmelCase_   ,       )
              video_inputs.append(UpperCAmelCase_      )

       return video_inputs






class        UpperCAmelCase__				:



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Tuple     					=    None
     def        lowerCAmelCase__  (				self	):
            a			   =self.image_processing_class(**self.image_processor_dict	)
            a			   =json.loads(image_processor.to_json_string()	)
            for key, value in self.image_processor_dict.items():
                   self.assertEqual(obj[key] ,    _lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.image_processing_class(**self.image_processor_dict	)

            with tempfile.TemporaryDirectory() as tmpdirname:
                   a			   =os.path.join(_lowerCAmelCase ,    """image_processor.json"""	)
                   image_processor_first.to_json_file(_lowerCAmelCase	)
                   a			   =self.image_processing_class.from_json_file(_lowerCAmelCase	)

            self.assertEqual(image_processor_second.to_dict() ,    image_processor_first.to_dict()	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.image_processing_class(**self.image_processor_dict	)

            with tempfile.TemporaryDirectory() as tmpdirname:
                   a			   =image_processor_first.save_pretrained(_lowerCAmelCase	)[0]
                   check_json_file_has_correct_format(_lowerCAmelCase	)
                   a			   =self.image_processing_class.from_pretrained(_lowerCAmelCase	)

            self.assertEqual(image_processor_second.to_dict() ,    image_processor_first.to_dict()	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.image_processing_class()
            self.assertIsNotNone(_lowerCAmelCase	)






     @require_torch
     @require_vision
     def        lowerCAmelCase__  (				self	):
            if self.test_cast_dtype is not None:
                   # Initialize image_processor
                   a			   =self.image_processing_class(**self.image_processor_dict	)

                   # create random PyTorch tensors
                   a			   =prepare_image_inputs(self.image_processor_tester ,    equal_resolution=_lowerCAmelCase ,    torchify=_lowerCAmelCase	)

                   a			   =image_processor(_lowerCAmelCase ,    return_tensors="""pt"""	)
                   # for layoutLM compatiblity
                   self.assertEqual(encoding.pixel_values.device ,    torch.device("""cpu"""	)	)
                   self.assertEqual(encoding.pixel_values.dtype ,    torch.floataa	)

                   a			   =image_processor(_lowerCAmelCase ,    return_tensors="""pt"""	).to(torch.floataa	)
                   self.assertEqual(encoding.pixel_values.device ,    torch.device("""cpu"""	)	)
                   self.assertEqual(encoding.pixel_values.dtype ,    torch.floataa	)

                   a			   =image_processor(_lowerCAmelCase ,    return_tensors="""pt"""	).to("""cpu""" ,    torch.bfloataa	)
                   self.assertEqual(encoding.pixel_values.device ,    torch.device("""cpu"""	)	)
                   self.assertEqual(encoding.pixel_values.dtype ,    torch.bfloataa	)

                   with self.assertRaises(_lowerCAmelCase	):
                          a			   =image_processor(_lowerCAmelCase ,    return_tensors="""pt"""	).to(torch.bfloataa ,    """cpu"""	)

                   # Try with text + image feature
                   a			   =image_processor(_lowerCAmelCase ,    return_tensors="""pt"""	)
                   encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]]	)}	)
                   a			   =encoding.to(torch.floataa	)

                   self.assertEqual(encoding.pixel_values.device ,    torch.device("""cpu"""	)	)
                   self.assertEqual(encoding.pixel_values.dtype ,    torch.floataa	)
                   self.assertEqual(encoding.input_ids.dtype ,    torch.long	)






import math
import os

import cva
import numpy as np

_lowerCamelCase							=      255.0





def 							lowerCamelCase    (							UpperCAmelCase_	:      float   ,       UpperCAmelCase_	:      float      )->			float:

       """simple docstring"""




       a			   =np.mean((original - contrast) ** 2      )
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase_      )      )





def 							lowerCamelCase    (							)->			None:

       """simple docstring"""




       a			   =os.path.dirname(os.path.realpath(UpperCAmelCase_      )      )
       # Loading images (original image and compressed image)
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/original_image.png"""      )      )
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/compressed_image.png"""      )   ,       1      )

       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-base.png"""      )      )
       a			   =cva.imread(
           os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-comp-10.jpg"""      )   ,       1      )

       # Value expected: 29.73dB
       print("""-- First Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )

       # # Value expected: 31.53dB (Wikipedia Example)
       print("""\n-- Second Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )


if __name__ == "__main__":
    main()
