






"""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__       (  snake_case       : Union[str, Any]			,					snake_case       : List[Any]				)->		Dict:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							k_size // 2
     UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     UpperCAmelCase__						:    str							=							1 / (2 * pi * sigma) * exp(-(square(snake_case				) + square(snake_case				)) / (2 * square(snake_case				))				)
     return g


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]			,					snake_case       : List[str]			,					snake_case       : int				)->		str:






     '''simple docstring'''



     UpperCAmelCase__							,				UpperCAmelCase__						:    List[str]							=							image.shape[0], image.shape[1]
     # dst image height and width
     UpperCAmelCase__						:    Any							=							height - k_size + 1
     UpperCAmelCase__						:    List[Any]							=							width - k_size + 1

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

     #  turn the kernel into shape(k*k, 1)
     UpperCAmelCase__						:    int							=							gen_gaussian_kernel(snake_case			,					snake_case				)
     UpperCAmelCase__						:    int							=							ravel(snake_case				)

     # reshape and get the dst image
     UpperCAmelCase__						:    Any							=							dot(snake_case			,					snake_case				).reshape(snake_case			,					snake_case				).astype(snake_case				)

     return dst


if __name__ == "__main__":
 # read original image
 _lowerCAmelCase	:	str				=      imread(r"""../image_data/lena.jpg""")
 # turn image in gray scale value
 _lowerCAmelCase	:	Dict				=      cvtColor(img, COLOR_BGR2GRAY)

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










"""simple docstring"""



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	float							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	str							,			snake_case__      :	bool = False							,			):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Optional[int]							=							nn.Embedding(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							nn.Embedding(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							False

        UpperCAmelCase__						:    Optional[int]							=							nn.Dropout(p=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							TaConfig(
            vocab_size=snake_case__							,			d_model=snake_case__							,			num_heads=snake_case__							,			d_kv=snake_case__							,			d_ff=snake_case__							,			dropout_rate=snake_case__							,			feed_forward_proj=snake_case__							,			is_decoder=snake_case__							,			is_encoder_decoder=snake_case__							,			)

        UpperCAmelCase__						:    Tuple							=							nn.ModuleList()
        for lyr_num in range(snake_case__							):
             UpperCAmelCase__						:    Tuple							=							TaBlock(snake_case__							)
             self.encoders.append(snake_case__							)

        UpperCAmelCase__						:    str							=							TaLayerNorm(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							nn.Dropout(p=snake_case__							)




   def       __a							(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.token_embedder(snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							encoder_input_tokens.shape[1]
        UpperCAmelCase__						:    List[Any]							=							torch.arange(snake_case__							,			device=encoder_input_tokens.device							)
        x += self.position_encoding(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.dropout_pre(snake_case__							)

        # inverted the attention mask
        UpperCAmelCase__						:    List[Any]							=							encoder_input_tokens.size()
        UpperCAmelCase__						:    Tuple							=							self.get_extended_attention_mask(snake_case__							,			snake_case__							)

        for lyr in self.encoders:
             UpperCAmelCase__						:    Any							=							lyr(snake_case__							,			snake_case__							)[0]
        UpperCAmelCase__						:    Any							=							self.layer_norm(snake_case__							)

        return self.dropout_post(snake_case__							), encoder_inputs_mask


