






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 							UpperCAmelCase_     (		unittest.TestCase		):




     def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=7  ,	_lowerCAmelCase=3  ,	_lowerCAmelCase=18  ,	_lowerCAmelCase=30  ,	_lowerCAmelCase=400  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=[0.5, 0.5, 0.5]  ,	_lowerCAmelCase=[0.5, 0.5, 0.5]  ,	):
          UpperCAmelCase__			:     Optional[Any]										= size if size is not None else {"""height""": 20, """width""": 20}
          UpperCAmelCase__			:     Optional[Any]										= crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

          UpperCAmelCase__			:     str										= parent
          UpperCAmelCase__			:     Union[str, Any]										= batch_size
          UpperCAmelCase__			:     Any										= num_channels
          UpperCAmelCase__			:     List[Any]										= image_size
          UpperCAmelCase__			:     Union[str, Any]										= min_resolution
          UpperCAmelCase__			:     Union[str, Any]										= max_resolution
          UpperCAmelCase__			:     Optional[int]										= do_resize
          UpperCAmelCase__			:     int										= size
          UpperCAmelCase__			:     List[Any]										= do_center_crop
          UpperCAmelCase__			:     Any										= crop_size
          UpperCAmelCase__			:     List[Any]										= do_normalize
          UpperCAmelCase__			:     Dict										= image_mean
          UpperCAmelCase__			:     str										= image_std




     def      __UpperCAmelCase     (							self		):
          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 							UpperCAmelCase_     (		__lowerCamelCase       ,			unittest.TestCase		):
     __lowerCamelCase										=     DeiTImageProcessor if is_vision_available() else None
     __lowerCamelCase										=     True




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Union[str, Any]										= DeiTImageProcessingTester(self		)




     @property
     def      __UpperCAmelCase     (							self		):
          return self.image_processor_tester.prepare_image_processor_dict()




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     str										= self.image_processing_class(**self.image_processor_dict		)
          self.assertTrue(hasattr(_lowerCAmelCase  ,	"""do_resize"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase  ,	"""size"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase  ,	"""do_center_crop"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase  ,	"""center_crop"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase  ,	"""do_normalize"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase  ,	"""image_mean"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase  ,	"""image_std"""		)		)




     def      __UpperCAmelCase     (							self		):
          UpperCAmelCase__			:     Optional[int]										= 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__			:     Tuple										= 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      __UpperCAmelCase     (							self		):
          pass




     def      __UpperCAmelCase     (							self		):
          # Initialize image_processing
          UpperCAmelCase__			:     Tuple										= self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          UpperCAmelCase__			:     Optional[Any]										= prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=_lowerCAmelCase		)
          for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase  ,	Image.Image		)

          # Test not batched input
          UpperCAmelCase__			:     Union[str, 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.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )  ,	)

          # Test batched
          UpperCAmelCase__			:     List[Any]										= image_processing(_lowerCAmelCase  ,	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      __UpperCAmelCase     (							self		):
          # Initialize image_processing
          UpperCAmelCase__			:     Dict										= self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          UpperCAmelCase__			:     Dict										= prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=_lowerCAmelCase  ,	numpify=_lowerCAmelCase		)
          for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase  ,	np.ndarray		)

          # Test not batched input
          UpperCAmelCase__			:     List[str]										= 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__			:     Optional[Any]										= image_processing(_lowerCAmelCase  ,	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      __UpperCAmelCase     (							self		):
          # Initialize image_processing
          UpperCAmelCase__			:     Optional[int]										= self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          UpperCAmelCase__			:     Optional[int]										= prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=_lowerCAmelCase  ,	torchify=_lowerCAmelCase		)
          for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase  ,	torch.Tensor		)

          # Test not batched input
          UpperCAmelCase__			:     str										= 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__			:     Dict										= image_processing(_lowerCAmelCase  ,	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"""],
              )  ,	)














import math
import os

import cva
import numpy as np

SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							255.0





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> float:

     '''simple docstring'''
     UpperCAmelCase__			:     List[Any]										= np.mean((original - contrast) ** 2					)
     if mse == 0:
          return 100

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





def    _lowerCamelCase	(	)   -> None:

     '''simple docstring'''
     UpperCAmelCase__			:     Union[str, Any]										= os.path.dirname(os.path.realpath(__lowerCamelCase					)					)
     # Loading images (original image and compressed image)
     UpperCAmelCase__			:     List[Any]										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/original_image.png"""					)					)
     UpperCAmelCase__			:     Dict										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/compressed_image.png"""					)   ,       1					)

     UpperCAmelCase__			:     Tuple										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-base.png"""					)					)
     UpperCAmelCase__			:     int										= cva.imread(
         os.path.join(__lowerCamelCase   ,       """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(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)

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


if __name__ == "__main__":
      main()






