





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 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Union[str, Any]						=			["""image_processor""", """tokenizer"""]
      _lowercase				:					Optional[Any]						=			"""BlipImageProcessor"""
      _lowercase				:					Any						=			"""AutoTokenizer"""







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=False
        super().__init__(lowerCAmelCase__		,      lowerCAmelCase__							)
        a__	:					Tuple										=self.image_processor







      def __call__(    self		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = 0		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__		,      )   ->						BatchEncoding:



        '''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__	:					Tuple										=self.tokenizer
          a__	:					str										=self.tokenizer(
              text=lowerCAmelCase__		,      add_special_tokens=lowerCAmelCase__		,      padding=lowerCAmelCase__		,      truncation=lowerCAmelCase__		,      max_length=lowerCAmelCase__		,      stride=lowerCAmelCase__		,      pad_to_multiple_of=lowerCAmelCase__		,      return_attention_mask=lowerCAmelCase__		,      return_overflowing_tokens=lowerCAmelCase__		,      return_special_tokens_mask=lowerCAmelCase__		,      return_offsets_mapping=lowerCAmelCase__		,      return_token_type_ids=lowerCAmelCase__		,      return_length=lowerCAmelCase__		,      verbose=lowerCAmelCase__		,      return_tensors=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
          return text_encoding

        # add pixel_values
        a__	:					Dict										=self.image_processor(lowerCAmelCase__		,      return_tensors=lowerCAmelCase__							)

        if text is not None:
          a__	:					List[str]										=self.tokenizer(
              text=lowerCAmelCase__		,      add_special_tokens=lowerCAmelCase__		,      padding=lowerCAmelCase__		,      truncation=lowerCAmelCase__		,      max_length=lowerCAmelCase__		,      stride=lowerCAmelCase__		,      pad_to_multiple_of=lowerCAmelCase__		,      return_attention_mask=lowerCAmelCase__		,      return_overflowing_tokens=lowerCAmelCase__		,      return_special_tokens_mask=lowerCAmelCase__		,      return_offsets_mapping=lowerCAmelCase__		,      return_token_type_ids=lowerCAmelCase__		,      return_length=lowerCAmelCase__		,      verbose=lowerCAmelCase__		,      return_tensors=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
        else:
          a__	:					List[str]										=None

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

        return encoding_image_processor







      def    _lowercase  (    self		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




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







      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




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







"""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     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       int	)       ->      List[str]:
  _lowerCAmelCase						:       Tuple  			=     k_size // 2
  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _lowerCAmelCase						:       Union[str, Any]  			=     1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase	) + square(_lowerCamelCase	)) / (2 * square(_lowerCamelCase	))	)
  return g





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       int			,_lowerCamelCase						:       int	)       ->      Dict:
  _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     image.shape[0], image.shape[1]
  # dst image height and width
  _lowerCAmelCase						:       Optional[int]  			=     height - k_size + 1
  _lowerCAmelCase						:       Dict  			=     width - k_size + 1

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

  #  turn the kernel into shape(k*k, 1)
  _lowerCAmelCase						:       List[Any]  			=     gen_gaussian_kernel(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     ravel(_lowerCamelCase	)

  # reshape and get the dst image
  _lowerCAmelCase						:       int  			=     dot(_lowerCamelCase			,_lowerCamelCase	).reshape(_lowerCamelCase			,_lowerCamelCase	).astype(_lowerCamelCase	)

  return dst


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

     # get values with two different mask size
     _a      :		Union[str, Any]           =   gaussian_filter(gray, 3, sigma=1)
     _a      :		List[Any]           =   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()




