






"""simple docstring"""
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 		a_      (	unittest.TestCase ):







      '''simple docstring'''

      def __init__(self,     lowerCamelCase_,     lowerCamelCase_=7,     lowerCamelCase_=3,     lowerCamelCase_=1_8,     lowerCamelCase_=3_0,     lowerCamelCase_=4_0_0,     lowerCamelCase_=True,     lowerCamelCase_=None,     lowerCamelCase_=True,     lowerCamelCase_=[0.5, 0.5, 0.5],     lowerCamelCase_=[0.5, 0.5, 0.5],     ):

       '''simple docstring'''



       lowerCamelCase__     :			str     				=						size if size is not None else {'height': 1_8, 'width': 1_8}
       lowerCamelCase__     :			Any     				=						parent
       lowerCamelCase__     :			Union[str, Any]     				=						batch_size
       lowerCamelCase__     :			Optional[Any]     				=						num_channels
       lowerCamelCase__     :			Tuple     				=						image_size
       lowerCamelCase__     :			Dict     				=						min_resolution
       lowerCamelCase__     :			Any     				=						max_resolution
       lowerCamelCase__     :			Optional[int]     				=						do_resize
       lowerCamelCase__     :			Union[str, Any]     				=						size
       lowerCamelCase__     :			str     				=						do_normalize
       lowerCamelCase__     :			List[str]     				=						image_mean
       lowerCamelCase__     :			Union[str, Any]     				=						image_std





      def 			a__							(self					):

       '''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 		a_      (	snake_case_  ,						unittest.TestCase ):







      '''simple docstring'''

      lowerCamelCase__       :				Any =	ViTImageProcessor if is_vision_available() else None




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						ViTImageProcessingTester(self					)




      @property
      def 			a__							(self					):

       '''simple docstring'''



       return self.image_processor_tester.prepare_image_processor_dict()




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Any     				=						self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(lowerCamelCase_,     'image_mean'					)					)
       self.assertTrue(hasattr(lowerCamelCase_,     'image_std'					)					)
       self.assertTrue(hasattr(lowerCamelCase_,     'do_normalize'					)					)
       self.assertTrue(hasattr(lowerCamelCase_,     'do_resize'					)					)
       self.assertTrue(hasattr(lowerCamelCase_,     'size'					)					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			List[str]     				=						self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size,     {'height': 1_8, 'width': 1_8}					)

       lowerCamelCase__     :			Optional[Any]     				=						self.image_processing_class.from_dict(self.image_processor_dict,     size=4_2					)
       self.assertEqual(image_processor.size,     {'height': 4_2, 'width': 4_2}					)




      def 			a__							(self					):

       '''simple docstring'''



       pass




      def 			a__							(self					):

       '''simple docstring'''



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

       # Test not batched input
       lowerCamelCase__     :			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.size['height'],
               self.image_processor_tester.size['width'],
           ),     )

       # Test batched
       lowerCamelCase__     :			int     				=						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.size['height'],
               self.image_processor_tester.size['width'],
           ),     )




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			List[Any]     				=						self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       lowerCamelCase__     :			Tuple     				=						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
       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(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.size['height'],
               self.image_processor_tester.size['width'],
           ),     )





      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       lowerCamelCase__     :			Dict     				=						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
       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__     :			int     				=						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.size['height'],
               self.image_processor_tester.size['width'],
           ),     )









"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__				,     lowerCamelCase__     :			Optional[Any]     				=						img.shape[0], img.shape[1]

 def to_grayscale(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
  return 0.2_126 * red + 0.587 * green + 0.114 * blue

 def normalize(_lowerCamelCase				):
  return min(_lowerCamelCase			,   255				)

 for i in range(_lowerCamelCase				):
  for j in range(_lowerCamelCase				):
   lowerCamelCase__     :			List[Any]     				=						int(to_grayscale(*img[i][j]				)				)
   lowerCamelCase__     :			Optional[int]     				=						[
       normalize(_lowerCamelCase				),
       normalize(greyscale + factor				),
       normalize(greyscale + 2 * factor				),
   ]

 return img


if __name__ == "__main__":
      # read original image
      A_			: List[str]				=			{
          percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
      }

      for percentage, img in images.items():
            make_sepia(img, percentage)

      for percentage, img in images.items():
            imshow(f"Original image with sepia (factor: {percentage})", img)

      waitKey(0)
      destroyAllWindows()

