



import warnings

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



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Tuple       				=    ["image_processor", "tokenizer"]
   A    :  List[Any]       				=    "ChineseCLIPImageProcessor"
   A    :  List[str]       				=    ("BertTokenizer", "BertTokenizerFast")


   def __init__(						self  :			List[Any]			,				lowerCAmelCase  :			str=None			,				lowerCAmelCase  :			Optional[Any]=None			,				**lowerCAmelCase  :			int)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  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			,				)
               lowercase__     =  kwargs.pop('feature_extractor')

         lowercase__     =  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)
         lowercase__     =  self.image_processor


   def __call__(						self  :			Dict			,				lowerCAmelCase  :			List[str]=None			,				lowerCAmelCase  :			List[str]=None			,				lowerCAmelCase  :			int=None			,				**lowerCAmelCase  :			Optional[Any])	->				Any:




         """simple docstring"""

         if text is None and images is None:
               raise ValueError('You have to specify either text or images. Both cannot be none.')

         if text is not None:
               lowercase__     =  self.tokenizer(lowerCAmelCase			,				return_tensors=lowerCAmelCase			,				**lowerCAmelCase)

         if images is not None:
               lowercase__     =  self.image_processor(lowerCAmelCase			,				return_tensors=lowerCAmelCase			,				**lowerCAmelCase)

         if text is not None and images is not None:
               lowercase__     =  image_features.pixel_values
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**lowerCAmelCase)			,				tensor_type=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Tuple			,				*lowerCAmelCase  :			Optional[Any]			,				**lowerCAmelCase  :			List[str])	->				Tuple:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Dict			,				*lowerCAmelCase  :			Union[str, Any]			,				**lowerCAmelCase  :			int)	->				int:




         """simple docstring"""

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


   @property
   def 			UpperCAmelCase		(						self  :			List[str])	->				Dict:




         """simple docstring"""

         lowercase__     =  self.tokenizer.model_input_names
         lowercase__     =  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



   @property
   def 			UpperCAmelCase		(						self  :			Any)	->				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





import cva
import numpy as np



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			float			,				lowerCAmelCase  :			int)	->				Dict:




         """simple docstring"""

         if k in (0.04, 0.06):
               lowercase__     =  k
               lowercase__     =  window_size
         else:
               raise ValueError('invalid k value')


   def __str__(						self  :			Tuple)	->				str:




         """simple docstring"""

         return str(self.k)



   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			str)	->				tuple[cva.Mat, list[list[int]]]:




         """simple docstring"""

         lowercase__     =  cva.imread(lowerCAmelCase			,				0)
         lowercase__,	lowercase__     =  img.shape
         lowercase__     =  []
         lowercase__     =  img.copy()
         lowercase__     =  cva.cvtColor(lowerCAmelCase			,				cva.COLOR_GRAY2RGB)
         lowercase__,	lowercase__     =  np.gradient(lowerCAmelCase)
         lowercase__     =  dx**2
         lowercase__     =  dy**2
         lowercase__     =  dx * dy
         lowercase__     =  0.04
         lowercase__     =  self.window_size // 2
         for y in range(lowerCAmelCase			,				h - offset):
               for x in range(lowerCAmelCase			,				w - offset):
                     lowercase__     =  ixx[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()
                     lowercase__     =  iyy[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()
                     lowercase__     =  ixy[
                         y - offset : y + offset + 1, x - offset : x + offset + 1
                     ].sum()

                     lowercase__     =  (wxx * wyy) - (wxy**2)
                     lowercase__     =  wxx + wyy
                     lowercase__     =  det - k * (trace**2)
                     # Can change the value
                     if r > 0.5:
                           corner_list.append([x, y, r])
                           color_img.itemset((y, x, 0)			,				0)
                           color_img.itemset((y, x, 1)			,				0)
                           color_img.itemset((y, x, 2)			,				2_55)
         return color_img, corner_list


if __name__ == "__main__":
       a__      :				Dict   	=			HarrisCorner(0.0_4, 3)
       a__	,		a__      :				Dict   	=			edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)
