




import math
import sys

import cva
import numpy as np




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	np.ndarray:

    """simple docstring"""


    __UpperCamelCase  		=						math.sqrt(lowercase_      )
    __UpperCamelCase  		=						1 / (sigma * math.sqrt(2 * math.pi      ))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	np.ndarray:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	np.ndarray:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )       ->	np.ndarray:

    """simple docstring"""


    __UpperCamelCase  		=						np.zeros(img.shape      )
    __UpperCamelCase  		=						get_gauss_kernel(lowercase_					,     lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase  		=						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      ):
            __UpperCamelCase  		=						get_slice(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )
            __UpperCamelCase  		=						img_s - img_s[kernel_size // 2, kernel_size // 2]
            __UpperCamelCase  		=						vec_gaussian(lowercase_					,     lowercase_      )
            __UpperCamelCase  		=						np.multiply(lowercase_					,     lowercase_      )
            __UpperCamelCase  		=						np.multiply(lowercase_					,     lowercase_      )
            __UpperCamelCase  		=						np.sum(lowercase_      ) / np.sum(lowercase_      )
            __UpperCamelCase  		=						val
    return imga




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	tuple:

    """simple docstring"""


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


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

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












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["image_processor", "tokenizer"]
      lowerCAmelCase__			: List[Any]    =					"ViTImageProcessor"
      lowerCAmelCase__			: Optional[int]    =					("CLIPTokenizer", "CLIPTokenizerFast")







      def __init__(     self					: Any ,       snake_case					: Any=None ,       snake_case					: Dict=None ,       **snake_case					: Tuple				):
          __UpperCamelCase  		=						None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.''' ,       snake_case ,       )
              __UpperCamelCase  		=						kwargs.pop('''feature_extractor'''				)

          __UpperCamelCase  		=						image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('''You need to specify an `image_processor`.'''				)
          if tokenizer is None:
              raise ValueError('''You need to specify a `tokenizer`.'''				)

          super().__init__(snake_case ,       snake_case				)







      def __call__(     self					: Dict ,       snake_case					: Union[str, Any]=None ,       snake_case					: Tuple=None ,       snake_case					: Any=None ,       snake_case					: Any=None ,       **snake_case					: Optional[int]				):
          if text is None and visual_prompt is None and images is None:
              raise ValueError('''You have to specify either text, visual prompt or images.'''				)

          if text is not None and visual_prompt is not None:
              raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

          if text is not None:
              __UpperCamelCase  		=						self.tokenizer(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if images is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None and images is not None:
              __UpperCamelCase  		=						{
                  '''pixel_values''': image_features.pixel_values,
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          elif text is not None and images is not None:
              __UpperCamelCase  		=						image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          elif visual_prompt is not None:
              __UpperCamelCase  		=						{
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          else:
              return BatchEncoding(data=dict(**snake_case				) ,       tensor_type=snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: Any				):
          return self.tokenizer.batch_decode(*snake_case ,       **snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Dict ,       **snake_case					: List[str]				):
          return self.tokenizer.decode(*snake_case ,       **snake_case				)







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,       snake_case ,       )
          return self.image_processor_class







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,       snake_case ,       )
          return self.image_processor






