import cva
import numpy as np







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						int:

     try:
          return int(image[x_coordinate][y_coordinate] >= center							)
     except (IndexError, TypeError):
          return 0







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						int:
     a	:     Any						      =    image[x_coordinate][y_coordinate]
     a	:     Optional[Any]						      =    [1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

     # skip get_neighbors_pixel if center is null
     if center is None:
          return 0

     # Starting from the top right, assigning value to pixels clockwise
     a	:     str						      =    [
         get_neighbors_pixel(lowerCAmelCase_    ,   x_coordinate - 1    ,   y_coordinate + 1    ,   lowerCAmelCase_							),
         get_neighbors_pixel(lowerCAmelCase_    ,   lowerCAmelCase_    ,   y_coordinate + 1    ,   lowerCAmelCase_							),
         get_neighbors_pixel(lowerCAmelCase_    ,   x_coordinate - 1    ,   lowerCAmelCase_    ,   lowerCAmelCase_							),
         get_neighbors_pixel(lowerCAmelCase_    ,   x_coordinate + 1    ,   y_coordinate + 1    ,   lowerCAmelCase_							),
         get_neighbors_pixel(lowerCAmelCase_    ,   x_coordinate + 1    ,   lowerCAmelCase_    ,   lowerCAmelCase_							),
         get_neighbors_pixel(lowerCAmelCase_    ,   x_coordinate + 1    ,   y_coordinate - 1    ,   lowerCAmelCase_							),
         get_neighbors_pixel(lowerCAmelCase_    ,   lowerCAmelCase_    ,   y_coordinate - 1    ,   lowerCAmelCase_							),
         get_neighbors_pixel(lowerCAmelCase_    ,   x_coordinate - 1    ,   y_coordinate - 1    ,   lowerCAmelCase_							),
     ]

     # Converting the binary value to decimal.
     return sum(
         binary_value * power for binary_value, power in zip(lowerCAmelCase_    ,   lowerCAmelCase_							)							)


if __name__ == "__main__":
  # Reading the image and converting it to grayscale.
  UpperCAmelCase				 =   cva.imread(
      """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
  )

  # Create a numpy array as the same height and width of read image
  UpperCAmelCase				 =   np.zeros((image.shape[0], image.shape[1]))

  # Iterating through the image and calculating the
  # local binary pattern value for each pixel.
  for i in range(0, image.shape[0]):
    for j in range(0, image.shape[1]):
      UpperCAmelCase				 =   local_binary_value(image, i, j)

  cva.imshow("""local binary pattern""", lbp_image)
  cva.waitKey(0)
  cva.destroyAllWindows()



import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    IFImgaImgPipeline
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __snake_case				=    PipelineTesterMixin.required_optional_params - {"latents"}



 def 		UpperCamelCase__	(      self   ):
       return self._get_dummy_components()



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 32, 32)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

       snake_case_			= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_optional_components()



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						, reason='''XFormers attention is only available with CUDA and `xformers` installed'''						, )
 def 		UpperCamelCase__	(      self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       super().test_floataa_inference(expected_max_diff=1E-1   )



 def 		UpperCamelCase__	(      self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_local()





 def 		UpperCamelCase__	(      self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2						, )