



import math
import sys

import cva
import numpy as np






def 	_lowerCAmelCase						(				__lowerCamelCase   :     np.ndarray		,	__lowerCamelCase   :     float     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	math.sqrt(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Dict       =	1 / (sigma * math.sqrt(2 * math.pi     ))
 return cons * np.exp(-((img / sigma) ** 2) * 0.5     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     np.ndarray		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	kernel_size // 2
 return img[x - half : x + half + 1, y - half : y + half + 1]






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     float     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	np.zeros((kernel_size, kernel_size)     )
 for i in range(0		,	__lowerCamelCase     ):
  for j in range(0		,	__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Dict       =	math.sqrt(
       abs(i - kernel_size // 2     ) ** 2 + abs(j - kernel_size // 2     ) ** 2     )
 return vec_gaussian(__lowerCamelCase		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     np.ndarray		,	__lowerCamelCase   :     float		,	__lowerCamelCase   :     float		,	__lowerCamelCase   :     int		,	):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	np.zeros(img.shape     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	get_gauss_kernel(__lowerCamelCase		,	__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	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     ):
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	get_slice(__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    str       =	img_s - img_s[kernel_size // 2, kernel_size // 2]
   __SCREAMING_SNAKE_CASE       :    int       =	vec_gaussian(__lowerCamelCase		,	__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Tuple       =	np.multiply(__lowerCamelCase		,	__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Tuple       =	np.multiply(__lowerCamelCase		,	__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Dict       =	np.sum(__lowerCamelCase     ) / np.sum(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Tuple       =	val
 return imga






def 	_lowerCAmelCase						(				__lowerCamelCase   :     list     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    int       =	args[1] if args[1:] else "../image_data/lena.jpg"
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	float(args[2]     ) if args[2:] else 1.0
 __SCREAMING_SNAKE_CASE       :    List[str]       =	float(args[3]     ) if args[3:] else 1.0
 if args[4:]:
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	int(args[4]     )
  __SCREAMING_SNAKE_CASE       :    Tuple       =	kernel_size + abs(kernel_size % 2 - 1     )
 else:
  __SCREAMING_SNAKE_CASE       :    Dict       =	5
 return filename, spatial_variance, intensity_variance, kernel_size


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

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











import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict[str, int] = None     ,  UpperCamelCase :							int = 3_2     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Union[int, float] = 1 / 2_5_5     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,  UpperCamelCase :							Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict=7     ,  UpperCamelCase :							Dict=3_0     ,  UpperCamelCase :							Tuple=4_0_0     ,  UpperCamelCase :							Union[str, Any]=3     ,  )->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	parent
      __SCREAMING_SNAKE_CASE       :    str       =	do_resize
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	size if size is not None else {"shortest_edge": 2_8_8}
      __SCREAMING_SNAKE_CASE       :    List[str]       =	size_divisor
      __SCREAMING_SNAKE_CASE       :    str       =	do_rescale
      __SCREAMING_SNAKE_CASE       :    int       =	rescale_factor
      __SCREAMING_SNAKE_CASE       :    Tuple       =	do_normalize
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_center_crop
      __SCREAMING_SNAKE_CASE       :    List[str]       =	image_mean
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_std
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	do_pad
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	batch_size
      __SCREAMING_SNAKE_CASE       :    str       =	num_channels
      __SCREAMING_SNAKE_CASE       :    List[str]       =	min_resolution
      __SCREAMING_SNAKE_CASE       :    Tuple       =	max_resolution





     def 	__snake_case    (       self :							Optional[Any]				)->List[str]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }






     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Dict=False				)->Optional[Any]:
      if not batched:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.size["shortest_edge"]
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	image_inputs[0]
       if isinstance(UpperCamelCase     ,  Image.Image				):
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	image.size
       else:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	image.shape[1], image.shape[2]
       __SCREAMING_SNAKE_CASE       :    Tuple       =	size / min(UpperCamelCase     ,  UpperCamelCase				)
       if h < w:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	size, scale * w
       else:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	scale * h, size

       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	int((1_3_3_3 / 8_0_0) * size				)
       if max(UpperCamelCase     ,  UpperCamelCase				) > max_size:
        __SCREAMING_SNAKE_CASE       :    Any       =	max_size / max(UpperCamelCase     ,  UpperCamelCase				)
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	newh * scale
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	neww * scale

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	int(newh + 0.5				), int(neww + 0.5				)
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[]
       for image in image_inputs:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.get_expected_values([image]				)
        expected_values.append((expected_height, expected_width)				)
       __SCREAMING_SNAKE_CASE       :    int       =	max(UpperCamelCase     ,  key=lambda UpperCamelCase				: item[0]				)[0]
       __SCREAMING_SNAKE_CASE       :    List[str]       =	max(UpperCamelCase     ,  key=lambda UpperCamelCase				: item[1]				)[1]

      return expected_height, expected_width






@require_torch
@require_vision
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			BridgeTowerImageProcessor if is_vision_available() else None





     def 	__snake_case    (       self :							str				)->int:
      __SCREAMING_SNAKE_CASE       :    str       =	BridgeTowerImageProcessingTester(self				)





     @property
     def 	__snake_case    (       self :							str				)->Any:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 	__snake_case    (       self :							Optional[int]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "image_mean"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "image_std"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "do_normalize"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "do_resize"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "size"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "size_divisor"				)				)





     def 	__snake_case    (       self :							Optional[int]				)->int:
      pass





     def 	__snake_case    (       self :							str				)->Union[str, Any]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    Any       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  Image.Image				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    Any       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )





     def 	__snake_case    (       self :							List[str]				)->Optional[int]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase     ,  numpify=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  np.ndarray				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    str       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )






     def 	__snake_case    (       self :							Optional[Any]				)->Union[str, Any]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE       :    List[str]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase     ,  torchify=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  torch.Tensor				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    str       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Any       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )






