





"""simple docstring"""
from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    Any			):
 lowerCAmelCase    				= k_size // 2
 lowerCAmelCase				,lowerCAmelCase    				= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
 lowerCAmelCase    				= 1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase			) + square(_UpperCAmelCase			)) / (2 * square(_UpperCAmelCase			))			)
 return g

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    Optional[Any]			):
 lowerCAmelCase				,lowerCAmelCase    				= image.shape[0], image.shape[1]
 # dst image height and width
 lowerCAmelCase    				= height - k_size + 1
 lowerCAmelCase    				= width - k_size + 1

 # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
 lowerCAmelCase    				= zeros((dst_height * dst_width, k_size * k_size)			)
 lowerCAmelCase    				= 0
 for i, j in product(range(_UpperCAmelCase			)  ,     range(_UpperCAmelCase			)			):
  lowerCAmelCase    				= ravel(image[i : i + k_size, j : j + k_size]			)
  lowerCAmelCase    				= window
  row += 1

 #  turn the kernel into shape(k*k, 1)
 lowerCAmelCase    				= gen_gaussian_kernel(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= ravel(_UpperCAmelCase			)

 # reshape and get the dst image
 lowerCAmelCase    				= dot(_UpperCAmelCase  ,     _UpperCAmelCase			).reshape(_UpperCAmelCase  ,     _UpperCAmelCase			).astype(_UpperCAmelCase			)

 return dst


if __name__ == "__main__":
       # read original image
       __UpperCamelCase     :						Tuple						       =  imread(R'''../image_data/lena.jpg''')
       # turn image in gray scale value
       __UpperCamelCase     :						int						       =  cvtColor(img, COLOR_BGR2GRAY)

       # get values with two different mask size
       __UpperCamelCase     :						Optional[Any]						       =  gaussian_filter(gray, 3, sigma=1)
       __UpperCamelCase     :						Tuple						       =  gaussian_filter(gray, 5, sigma=0.8)

       # show result images
       imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
       imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
       waitKey()













from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Tuple     =   "BridgeTowerImageProcessor"
		__snake_case      :       List[str]     =   ("RobertaTokenizer", "RobertaTokenizerFast")







		def __init__(					self       ,A__       ,A__		):
						super().__init__(A__       ,A__		)







		def __call__(					self       ,A__       ,A__ = None       ,A__ = True       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = 0       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      self.tokenizer(
						    text=A__       ,add_special_tokens=A__       ,padding=A__       ,truncation=A__       ,max_length=A__       ,stride=A__       ,pad_to_multiple_of=A__       ,return_token_type_ids=A__       ,return_attention_mask=A__       ,return_overflowing_tokens=A__       ,return_special_tokens_mask=A__       ,return_offsets_mapping=A__       ,return_length=A__       ,verbose=A__       ,return_tensors=A__       ,**A__       ,)
						# add pixel_values + pixel_mask
						_A	:				Optional[Any]   =      self.image_processor(
						    A__       ,return_tensors=A__       ,do_normalize=A__       ,do_center_crop=A__       ,**A__		)
						encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.tokenizer.model_input_names
						_A	:				str   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



