





import math
import sys

import cva
import numpy as np




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     np.ndarray:
  # For applying gaussian function for each element in matrix.
  lowercase__		    =						math.sqrt(__lowerCAmelCase	)
  lowercase__		    =						1 / (sigma * math.sqrt(2 * math.pi	))
  return cons * np.exp(-((img / sigma) ** 2) * 0.5	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     np.ndarray:
  lowercase__		    =						kernel_size // 2
  return img[x - half : x + half + 1, y - half : y + half + 1]




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     np.ndarray:
  # Creates a gaussian kernel of given dimension.
  lowercase__		    =						np.zeros((kernel_size, kernel_size)	)
  for i in range(0	,		__lowerCAmelCase	):
    for j in range(0	,		__lowerCAmelCase	):
      lowercase__		    =						math.sqrt(
          abs(i - kernel_size // 2	) ** 2 + abs(j - kernel_size // 2	) ** 2	)
  return vec_gaussian(__lowerCAmelCase	,		__lowerCAmelCase	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		)       ->     np.ndarray:
  lowercase__		    =						np.zeros(img.shape	)
  lowercase__		    =						get_gauss_kernel(__lowerCAmelCase	,		__lowerCAmelCase	)
  lowercase__		    =						img.shape
  for i in range(kernel_size // 2	,		size_x - kernel_size // 2	):
    for j in range(kernel_size // 2	,		size_y - kernel_size // 2	):
      lowercase__		    =						get_slice(__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase	)
      lowercase__		    =						img_s - img_s[kernel_size // 2, kernel_size // 2]
      lowercase__		    =						vec_gaussian(__lowerCAmelCase	,		__lowerCAmelCase	)
      lowercase__		    =						np.multiply(__lowerCAmelCase	,		__lowerCAmelCase	)
      lowercase__		    =						np.multiply(__lowerCAmelCase	,		__lowerCAmelCase	)
      lowercase__		    =						np.sum(__lowerCAmelCase	) / np.sum(__lowerCAmelCase	)
      lowercase__		    =						val
  return imga




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     tuple:
  lowercase__		    =						args[1] if args[1:] else """../image_data/lena.jpg"""
  lowercase__		    =						float(args[2]	) if args[2:] else 1.0
  lowercase__		    =						float(args[3]	) if args[3:] else 1.0
  if args[4:]:
    lowercase__		    =						int(args[4]	)
    lowercase__		    =						kernel_size + abs(kernel_size % 2 - 1	)
  else:
    lowercase__		    =						5
  return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
      lowercase_					     =	parse_args(sys.argv)
      lowercase_					     =	cva.imread(filename, 0)
      cva.imshow("""input image""", img)

      lowercase_					     =	img / 255
      lowercase_					     =	out.astype("""float32""")
      lowercase_					     =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
      lowercase_					     =	out * 255
      lowercase_					     =	np.uinta(out)
      cva.imshow("""output image""", out)
      cva.waitKey(0)
      cva.destroyAllWindows()









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def __init__(					self    :       Any    ,  a    :       str    ,  a    :       List[Any]=7    ,  a    :       int=3    ,  a    :       int=18    ,  a    :       Optional[Any]=30    ,  a    :       Optional[int]=400    ,  a    :       int=True    ,  a    :       Tuple=None    ,  a    :       Optional[Any]=True    ,  a    :       str=False    ,  a    :       str=True    ,  a    :       int=True    ,  a    :       Tuple=[0.5, 0.5, 0.5]    ,  a    :       Any=[0.5, 0.5, 0.5]    ,  )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						num_channels
     lowercase__			    =						image_size
     lowercase__			    =						min_resolution
     lowercase__			    =						max_resolution
     lowercase__			    =						do_resize
     lowercase__			    =						size if size is not None else {'height': 18, 'width': 20}
     lowercase__			    =						do_thumbnail
     lowercase__			    =						do_align_axis
     lowercase__			    =						do_pad
     lowercase__			    =						do_normalize
     lowercase__			    =						image_mean
     lowercase__			    =						image_std


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_thumbnail": self.do_thumbnail,
         "do_align_long_axis": self.do_align_axis,
         "do_pad": self.do_pad,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Optional[Any]				      = DonutImageProcessor if is_vision_available() else None


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						DonutImageProcessingTester(self )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     int:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(a    ,  'do_resize' ) )
     self.assertTrue(hasattr(a    ,  'size' ) )
     self.assertTrue(hasattr(a    ,  'do_thumbnail' ) )
     self.assertTrue(hasattr(a    ,  'do_align_long_axis' ) )
     self.assertTrue(hasattr(a    ,  'do_pad' ) )
     self.assertTrue(hasattr(a    ,  'do_normalize' ) )
     self.assertTrue(hasattr(a    ,  'image_mean' ) )
     self.assertTrue(hasattr(a    ,  'image_std' ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size    ,  {'height': 18, 'width': 20} )

     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42 )
     self.assertEqual(image_processor.size    ,  {'height': 42, 'width': 42} )

     # Previous config had dimensions in (width, height) order
     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=(42, 84) )
     self.assertEqual(image_processor.size    ,  {'height': 84, 'width': 42} )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     pass


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  Image.Image )

     # Test not batched input
     lowercase__			    =						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
     lowercase__			    =						image_processing(a    ,  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'],
         )    ,  )


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a    ,  numpify=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  np.ndarray )

     # Test not batched input
     lowercase__			    =						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
     lowercase__			    =						image_processing(a    ,  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'],
         )    ,  )


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Dict:





     """simple docstring"""







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

     # Test not batched input
     lowercase__			    =						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
     lowercase__			    =						image_processing(a    ,  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'],
         )    ,  )


