





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Tuple	 =    "pegasus"
						__magic_name__:     str	 =    ["past_key_values"]
						__magic_name__:     int	 =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






						def __init__( self : Union[str, Any]						,				_A : Optional[Any]=50265						,				_A : List[str]=1024						,				_A : Optional[int]=12						,				_A : Any=4096						,				_A : str=16						,				_A : Dict=12						,				_A : Dict=4096						,				_A : Dict=16						,				_A : Optional[int]=0.0						,				_A : Dict=0.0						,				_A : Dict=True						,				_A : Optional[Any]=True						,				_A : Optional[int]="gelu"						,				_A : Tuple=1024						,				_A : int=0.1						,				_A : Optional[Any]=0.0						,				_A : str=0.0						,				_A : List[Any]=0.0_2						,				_A : Union[str, Any]=0						,				_A : str=False						,				_A : List[Any]=0						,				_A : List[str]=1						,				_A : List[Any]=1						,				**_A : Optional[Any]						,				)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		vocab_size
							snake_case_						:     str     =		max_position_embeddings
							snake_case_						:     List[str]     =		d_model
							snake_case_						:     List[str]     =		encoder_ffn_dim
							snake_case_						:     Optional[Any]     =		encoder_layers
							snake_case_						:     str     =		encoder_attention_heads
							snake_case_						:     Any     =		decoder_ffn_dim
							snake_case_						:     Optional[Any]     =		decoder_layers
							snake_case_						:     Tuple     =		decoder_attention_heads
							snake_case_						:     Dict     =		dropout
							snake_case_						:     List[str]     =		attention_dropout
							snake_case_						:     Any     =		activation_dropout
							snake_case_						:     int     =		activation_function
							snake_case_						:     Tuple     =		init_std
							snake_case_						:     int     =		encoder_layerdrop
							snake_case_						:     List[Any]     =		decoder_layerdrop
							snake_case_						:     Any     =		use_cache
							snake_case_						:     List[Any]     =		encoder_layers
							snake_case_						:     Optional[Any]     =		scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=_A						,				eos_token_id=_A						,				is_encoder_decoder=_A						,				decoder_start_token_id=_A						,				forced_eos_token_id=_A						,				**_A						,				)






						@property
						def UpperCAmelCase_			( self : List[Any]		)							->      int:







							"""simple docstring"""


							return self.encoder_attention_heads






						@property
						def UpperCAmelCase_			( self : Optional[int]		)							->      int:







							"""simple docstring"""


							return self.d_model








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__  (      __a				,	__a				):
	snake_case_						:     List[Any]     =		k_size // 2
	snake_case_  ,snake_case_						:     Any     =		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	snake_case_						:     Optional[Any]     =		1 / (2 * pi * sigma) * exp(-(square(__a				) + square(__a				)) / (2 * square(__a				))				)
	return g


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_  ,snake_case_						:     Union[str, Any]     =		image.shape[0], image.shape[1]
	# dst image height and width
	snake_case_						:     int     =		height - k_size + 1
	snake_case_						:     Optional[int]     =		width - k_size + 1

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

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

	# reshape and get the dst image
	snake_case_						:     Optional[int]     =		dot(__a				,	__a				).reshape(__a				,	__a				).astype(__a				)

	return dst


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

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

