import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey







class       __snake_case :
 def __init__(	self		, snake_case__		, snake_case__    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=0
       # max greyscale value for #FFFFFF
       UpperCAmelCase     :			str     	=int(self.get_greyscale(255		, 255		, 255    )    )

       if not self.min_threshold < threshold < self.max_threshold:
             UpperCAmelCase     :			List[Any]     	=f'''Factor value should be from 0 to {self.max_threshold}'''
             raise ValueError(snake_case__    )

       UpperCAmelCase     :			List[str]     	=input_img
       UpperCAmelCase     :			List[Any]     	=threshold
       UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=self.input_img.shape[1], self.input_img.shape[0]

       # error table size (+4 columns and +1 row) greater than input image because of
       # lack of if statements
       UpperCAmelCase     :			Tuple     	=[
           [0 for _ in range(self.height + 4    )] for __ in range(self.width + 1    )
       ]
       UpperCAmelCase     :			Union[str, Any]     	=np.ones((self.width, self.height, 3)		, np.uinta    ) * 255
 @classmethod
 def 						UpperCAmelCase__							(	cls		, snake_case__		, snake_case__		, snake_case__    )						->				float:
       '''simple docstring'''





       return 0.114 * blue + 0.587 * green + 0.2126 * red






 def 						UpperCAmelCase__							(	self    )						->				None:
       '''simple docstring'''





       for y in range(self.height    ):
             for x in range(self.width    ):
                   UpperCAmelCase     :			Optional[int]     	=int(self.get_greyscale(*self.input_img[y][x]    )    )
                   if self.threshold > greyscale + self.error_table[y][x]:
                         UpperCAmelCase     :			int     	=(0, 0, 0)
                         UpperCAmelCase     :			Union[str, Any]     	=greyscale + self.error_table[x][y]
                   else:
                         UpperCAmelCase     :			int     	=(255, 255, 255)
                         UpperCAmelCase     :			int     	=greyscale + self.error_table[x][y] - 255
                   self.error_table[y][x + 1] += int(8 / 32 * current_error    )
                   self.error_table[y][x + 2] += int(4 / 32 * current_error    )
                   self.error_table[y + 1][x] += int(8 / 32 * current_error    )
                   self.error_table[y + 1][x + 1] += int(4 / 32 * current_error    )
                   self.error_table[y + 1][x + 2] += int(2 / 32 * current_error    )
                   self.error_table[y + 1][x - 1] += int(4 / 32 * current_error    )
                   self.error_table[y + 1][x - 2] += int(2 / 32 * current_error    )


if __name__ == "__main__":
     # create Burke's instances with original images in greyscale
     __snake_case         =     [
         Burkes(imread('''image_data/lena.jpg''', 1), threshold)
         for threshold in (1, 1_26, 1_30, 1_40)
     ]

     for burkes in burkes_instances:
          burkes.process()

     for burkes in burkes_instances:
          imshow(
              f'Original image with dithering threshold: {burkes.threshold}',
              burkes.output_img,
          )

     waitKey(0)
     destroyAllWindows()

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 EfficientNetImageProcessor







class       __snake_case (  unittest.TestCase ):
 def __init__(	self		, snake_case__		, snake_case__=13		, snake_case__=3		, snake_case__=18		, snake_case__=30		, snake_case__=400		, snake_case__=True		, snake_case__=None		, snake_case__=True		, snake_case__=[0.5, 0.5, 0.5]		, snake_case__=[0.5, 0.5, 0.5]		, )						->				Any:
       '''simple docstring'''





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






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''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 (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   EfficientNetImageProcessor if is_vision_available() else None
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=EfficientNetImageProcessorTester(self    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(snake_case__		, '''image_mean'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''image_std'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_normalize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_resize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''size'''    )    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size		, {'''height''': 18, '''width''': 18}    )

       UpperCAmelCase     :			List[str]     	=self.image_processing_class.from_dict(self.image_processor_dict		, size=42    )
       self.assertEqual(image_processor.size		, {'''height''': 42, '''width''': 42}    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





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

       # Test not batched input
       UpperCAmelCase     :			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
       UpperCAmelCase     :			int     	=image_processing(snake_case__		, 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 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       UpperCAmelCase     :			List[str]     	=prepare_image_inputs(self.image_processor_tester		, equal_resolution=snake_case__		, numpify=snake_case__    )
       for image in image_inputs:
             self.assertIsInstance(snake_case__		, np.ndarray    )

       # 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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		, )

       # Test batched
       UpperCAmelCase     :			Union[str, Any]     	=image_processing(snake_case__		, 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 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





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

       # Test not batched input
       UpperCAmelCase     :			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
       UpperCAmelCase     :			List[str]     	=image_processing(snake_case__		, 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'''],
           )		, )
