






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 				_a     (   UpperCamelCase_    : Optional[Any]			,      UpperCamelCase_    : Optional[int]      )       ->      List[str]:
						"""simple docstring"""







						lowerCAmelCase__       					=  k_size // 2
						lowerCAmelCase__       					=  mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						lowerCAmelCase__       					=  1 / (2 * pi * sigma) * exp(-(square(__snake_case      ) + square(__snake_case      )) / (2 * square(__snake_case      ))      )
						return g


def 				_a     (   UpperCamelCase_    : List[str]			,      UpperCamelCase_    : List[str]			,      UpperCamelCase_    : int      )       ->      Union[str, Any]:
						"""simple docstring"""







						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(__snake_case      )			,      range(__snake_case      )      ):
												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(__snake_case			,      __snake_case      )
						lowerCAmelCase__       					=  ravel(__snake_case      )

						# reshape and get the dst image
						lowerCAmelCase__       					=  dot(__snake_case			,      __snake_case      ).reshape(__snake_case			,      __snake_case      ).astype(__snake_case      )

						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)

	# get values with two different mask size
	a_							=			gaussian_filter(gray, 3, sigma=1)
	a_							=			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()













'''simple docstring'''




from typing import List, Optional, Union

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


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       ['''image_processor''', '''tokenizer''']
     lowerCAmelCase					=       '''BlipImageProcessor'''
     lowerCAmelCase					=       ('''BertTokenizer''', '''BertTokenizerFast''')


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       False
          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Optional[int]      =       self.image_processor


     def __call__(						self						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True						,	_UpperCAmelCase = False						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = 0						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          if images is None and text is None:
               raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
               __A						: int      =       self.tokenizer
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
               return text_encoding

          # add pixel_values
          __A						: List[Any]      =       self.image_processor(_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)

          if text is not None:
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
          else:
               __A						: int      =       None

          if text_encoding is not None:
               encoding_image_processor.update(_UpperCAmelCase)

          return encoding_image_processor


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






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


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.tokenizer.model_input_names
          __A						: Tuple      =       self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))