

"""simple docstring"""




import warnings

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





class      __UpperCamelCase					(  _A		):
		SCREAMING_SNAKE_CASE														=       ["image_processor", "tokenizer"]
		SCREAMING_SNAKE_CASE														=       "AutoImageProcessor"
		SCREAMING_SNAKE_CASE														=       "AutoTokenizer"


		def __init__(self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=None	,	__SCREAMING_SNAKE_CASE					:						int=None	,	**__SCREAMING_SNAKE_CASE					:						Any):
							A     =   None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."	,	__SCREAMING_SNAKE_CASE	,	)
												A     =   kwargs.pop("feature_extractor")

							A     =   image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError("You have to specify an image_processor.")
							if tokenizer is None:
												raise ValueError("You have to specify a tokenizer.")

							super().__init__(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							A     =   self.image_processor


		def __call__(self					:						int	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=None	,	__SCREAMING_SNAKE_CASE					:						List[Any]=None	,	__SCREAMING_SNAKE_CASE					:						Tuple=None	,	**__SCREAMING_SNAKE_CASE					:						str):

							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:
												A     =   self.tokenizer(__SCREAMING_SNAKE_CASE	,	return_tensors=__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)

							if images is not None:
												A     =   self.image_processor(__SCREAMING_SNAKE_CASE	,	return_tensors=__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)

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


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


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	*__SCREAMING_SNAKE_CASE					:						int	,	**__SCREAMING_SNAKE_CASE					:						int):
							return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   self.tokenizer.model_input_names
							A     =   self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,	__SCREAMING_SNAKE_CASE	,	)
							return self.image_processor_class


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,	__SCREAMING_SNAKE_CASE	,	)
							return self.image_processor








"""simple docstring"""
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Tuple   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		Optional[Any]        =     image.shape
       __magic_name__			:		str        =     cols - block_size[1] + 1
       __magic_name__			:		int        =     rows - block_size[0] + 1
       __magic_name__			:		Optional[Any]        =     zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __magic_name__			:		List[Any]        =     0
       for i in range(0     , lowerCamelCase   ):
              for j in range(0     , lowerCamelCase   ):
                     __magic_name__			:		List[Any]        =     ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                     __magic_name__			:		str        =     window
                     row += 1

       return image_array







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[str]   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		List[str]        =     image.shape[0], image.shape[1]
       __magic_name__			:		Any        =     filter_kernel.shape[0]
       __magic_name__			:		Optional[Any]        =     k_size // 2
       # Pads image with the edge values of array.
       __magic_name__			:		Union[str, Any]        =     pad(lowerCamelCase     , lowerCamelCase     , mode='''edge'''   )

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __magic_name__			:		List[Any]        =     imacol(lowerCamelCase     , (k_size, k_size)   )

       #  turn the kernel into shape(k*k, 1)
       __magic_name__			:		Tuple        =     ravel(lowerCamelCase   )
       # reshape and get the dst image
       __magic_name__			:		int        =     dot(lowerCamelCase     , lowerCamelCase   ).reshape(lowerCamelCase     , lowerCamelCase   )
       return dst


if __name__ == "__main__":
 # read original image
 A									=						imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 A									=						cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 A									=						array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 A									=						img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)
