


"""simple docstring"""







from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__	,				UpperCAmelCase__		=    image.shape
						UpperCAmelCase__		=    cols - block_size[1] + 1
						UpperCAmelCase__		=    rows - block_size[0] + 1
						UpperCAmelCase__		=    zeros((dst_height * dst_width, block_size[1] * block_size[0]) )
						UpperCAmelCase__		=    0
						for i in range(0	,			_UpperCamelCase ):
												for j in range(0	,			_UpperCamelCase ):
																		UpperCAmelCase__		=    ravel(image[i : i + block_size[0], j : j + block_size[1]] )
																		UpperCAmelCase__		=    window
																		row += 1

						return image_array






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__	,				UpperCAmelCase__		=    image.shape[0], image.shape[1]
						UpperCAmelCase__		=    filter_kernel.shape[0]
						UpperCAmelCase__		=    k_size // 2
						# Pads image with the edge values of array.
						UpperCAmelCase__		=    pad(_UpperCamelCase	,			_UpperCamelCase	,			mode='''edge''' )

						# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
						UpperCAmelCase__		=    imacol(_UpperCamelCase	,			(k_size, k_size) )

						#  turn the kernel into shape(k*k, 1)
						UpperCAmelCase__		=    ravel(_UpperCamelCase )
						# reshape and get the dst image
						UpperCAmelCase__		=    dot(_UpperCamelCase	,			_UpperCamelCase ).reshape(_UpperCamelCase	,			_UpperCamelCase )
						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)
						# Laplace operator
						__a			 =   array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
						__a			 =   img_convolve(gray, Laplace_kernel).astype(uinta)
						imshow("Laplacian", out)
						waitKey(0)



"""simple docstring"""







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


__a			 =   logging.get_logger(__name__)

__a			 =   {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      """pegasus_x"""
							SCREAMING_SNAKE_CASE										=      ["""past_key_values"""]
							SCREAMING_SNAKE_CASE										=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(						self :     int		, lowerCamelCase_ :     Any=96103		, lowerCamelCase_ :     Union[str, Any]=16384		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     str=4096		, lowerCamelCase_ :     Tuple=16		, lowerCamelCase_ :     Optional[int]=16		, lowerCamelCase_ :     Tuple=4096		, lowerCamelCase_ :     Any=16		, lowerCamelCase_ :     List[str]=0.0		, lowerCamelCase_ :     int=0.0		, lowerCamelCase_ :     List[Any]=True		, lowerCamelCase_ :     str=True		, lowerCamelCase_ :     int="gelu"		, lowerCamelCase_ :     Optional[Any]=1024		, lowerCamelCase_ :     Optional[int]=0.1		, lowerCamelCase_ :     str=0.0		, lowerCamelCase_ :     Optional[Any]=0.0		, lowerCamelCase_ :     List[str]=0.02		, lowerCamelCase_ :     Optional[Any]=0		, lowerCamelCase_ :     List[str]=True		, lowerCamelCase_ :     List[Any]=0		, lowerCamelCase_ :     str=1		, lowerCamelCase_ :     Optional[Any]=1		, lowerCamelCase_ :     Tuple=32		, lowerCamelCase_ :     Dict=512		, lowerCamelCase_ :     List[Any]=True		, **lowerCamelCase_ :     int		, ):
													UpperCAmelCase__		=    vocab_size
													UpperCAmelCase__		=    max_position_embeddings
													UpperCAmelCase__		=    d_model
													UpperCAmelCase__		=    encoder_ffn_dim
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    encoder_attention_heads
													UpperCAmelCase__		=    decoder_ffn_dim
													UpperCAmelCase__		=    decoder_layers
													UpperCAmelCase__		=    decoder_attention_heads
													UpperCAmelCase__		=    dropout
													UpperCAmelCase__		=    attention_dropout
													UpperCAmelCase__		=    activation_dropout
													UpperCAmelCase__		=    activation_function
													UpperCAmelCase__		=    init_std
													UpperCAmelCase__		=    encoder_layerdrop
													UpperCAmelCase__		=    decoder_layerdrop
													UpperCAmelCase__		=    use_cache
													UpperCAmelCase__		=    encoder_layers
													UpperCAmelCase__		=    scale_embedding  # scale factor will be sqrt(d_model) if True

													UpperCAmelCase__		=    num_global_tokens
													UpperCAmelCase__		=    block_size
													UpperCAmelCase__		=    stagger_local_blocks

													super().__init__(
													    pad_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, is_encoder_decoder=lowerCamelCase_		, decoder_start_token_id=lowerCamelCase_		, forced_eos_token_id=lowerCamelCase_		, **lowerCamelCase_		, )






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.encoder_attention_heads






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													return self.d_model