




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class     snake_case  (	_snake_case							):


   '''simple docstring'''







   UpperCamelCase__	:       Optional[Any]     =      ["image_processor", "tokenizer"]
   UpperCamelCase__	:       Dict     =      "AutoImageProcessor"
   UpperCamelCase__	:       int     =      "AutoTokenizer"




   def __init__(					self     :	Dict						,    lowerCamelCase_     :	Any=None						,    lowerCamelCase_     :	Dict=None						,    **lowerCamelCase_     :	Tuple			)					->Dict:
     '''simple docstring'''







     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."""						,    lowerCamelCase_						,    )
       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__(lowerCamelCase_						,    lowerCamelCase_			)
     UpperCAmelCase__    						=   self.image_processor
     UpperCAmelCase__    						=   False




   def __call__(					self     :	Optional[int]						,    *lowerCamelCase_     :	List[Any]						,    **lowerCamelCase_     :	List[str]			)					->Any:
     '''simple docstring'''







     if self._in_target_context_manager:
       return self.current_processor(*lowerCamelCase_						,    **lowerCamelCase_			)

     UpperCAmelCase__    						=   kwargs.pop("""images"""						,    lowerCamelCase_			)
     UpperCAmelCase__    						=   kwargs.pop("""text"""						,    lowerCamelCase_			)
     if len(lowerCamelCase_			) > 0:
       UpperCAmelCase__    						=   args[0]
       UpperCAmelCase__    						=   args[1:]

     if images is None and text is None:
       raise ValueError("""You need to specify either an `images` or `text` input to process."""			)

     if images is not None:
       UpperCAmelCase__    						=   self.image_processor(lowerCamelCase_						,    *lowerCamelCase_						,    **lowerCamelCase_			)
     if text is not None:
       UpperCAmelCase__    						=   self.tokenizer(lowerCamelCase_						,    **lowerCamelCase_			)

     if text is None:
       return inputs
     elif images is None:
       return encodings
     else:
       UpperCAmelCase__    						=   encodings["""input_ids"""]
       return inputs




   def 	UpperCAmelCase       (					self     :	Optional[int]						,    *lowerCamelCase_     :	List[str]						,    **lowerCamelCase_     :	List[Any]			)					->List[str]:
     '''simple docstring'''







     return self.tokenizer.batch_decode(*lowerCamelCase_						,    **lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	List[Any]						,    *lowerCamelCase_     :	Optional[int]						,    **lowerCamelCase_     :	Union[str, Any]			)					->Union[str, Any]:
     '''simple docstring'''







     return self.tokenizer.decode(*lowerCamelCase_						,    **lowerCamelCase_			)




   @contextmanager
   def 	UpperCAmelCase       (					self     :	int			)					->int:
     '''simple docstring'''







     warnings.warn(
         """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
         """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
         """your images inputs, or in a separate call."""			)
     UpperCAmelCase__    						=   True
     UpperCAmelCase__    						=   self.tokenizer
     yield
     UpperCAmelCase__    						=   self.image_processor
     UpperCAmelCase__    						=   False




   @property
   def 	UpperCAmelCase       (					self     :	Tuple			)					->Tuple:
     '''simple docstring'''







     warnings.warn(
         """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,    lowerCamelCase_						,    )
     return self.image_processor_class







   @property
   def 	UpperCAmelCase       (					self     :	Optional[Any]			)					->Dict:
     '''simple docstring'''







     warnings.warn(
         """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						,    lowerCamelCase_						,    )
     return self.image_processor










from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like


def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase=3      ):
  UpperCAmelCase__    						=   int(mask / 2      )
  # copy image size
  UpperCAmelCase__    						=   zeros_like(_lowerCAmelCase      )
  for i in range(_lowerCAmelCase   ,     gray_img.shape[0] - bd      ):
    for j in range(_lowerCAmelCase   ,     gray_img.shape[1] - bd      ):
      # get mask according with mask
      UpperCAmelCase__    						=   ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
      # calculate mask median
      UpperCAmelCase__    						=   sort(_lowerCAmelCase      )[inta(divide((multiply(_lowerCAmelCase   ,     _lowerCAmelCase      ))   ,     2      ) + 1      )]
      UpperCAmelCase__    						=   median
  return median_img


if __name__ == "__main__":
   # read original image
   snake_case__ :						Tuple     =			imread('''../image_data/lena.jpg''')
   # turn image in gray scale value
   snake_case__ :						List[Any]     =			cvtColor(img, COLOR_BGR2GRAY)

   # get values with two different mask size
   snake_case__ :						str     =			median_filter(gray, 3)
   snake_case__ :						List[str]     =			median_filter(gray, 5)

   # show result images
   imshow('''median filter with 3x3 mask''', medianaxa)
   imshow('''median filter with 5x5 mask''', medianaxa)
   waitKey(0)




