


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


lowerCamelCase						 =	logging.get_logger(__name__)

lowerCamelCase						 =	{
    'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json'
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}




class A  (       UpperCamelCase_					):
      UpperCamelCase__							:  Optional[Any]				='cpmant'






      def __init__( self						:					int      ,       lowercase_						:					int = 3_0720      ,       lowercase_						:					int = 4096      ,       lowercase_						:					int = 32      ,       lowercase_						:					int = 128      ,       lowercase_						:					int = 1_0240      ,       lowercase_						:					int = 48      ,       lowercase_						:					int = 0.0      ,       lowercase_						:					int = 512      ,       lowercase_						:					int = 2048      ,       lowercase_						:					int = 1E-6      ,       lowercase_						:					float = 1.0      ,       lowercase_						:					int = 32      ,       lowercase_						:					int = 32      ,       lowercase_						:					int = 32      ,       lowercase_						:					bool = True      ,       **lowercase_						:					int      ,       ) ->						int:
             """simple docstring"""




             super().__init__(**lowercase_			)
             _lowerCamelCase	:		Optional[int]          =prompt_types
             _lowerCamelCase	:		Union[str, Any]          =prompt_length
             _lowerCamelCase	:		str          =segment_types
             _lowerCamelCase	:		Optional[int]          =hidden_size
             _lowerCamelCase	:		Tuple          =num_attention_heads
             _lowerCamelCase	:		Optional[int]          =dim_head
             _lowerCamelCase	:		Any          =dim_ff
             _lowerCamelCase	:		Tuple          =num_hidden_layers
             _lowerCamelCase	:		Tuple          =position_bias_num_buckets
             _lowerCamelCase	:		Optional[int]          =position_bias_max_distance
             _lowerCamelCase	:		List[str]          =dropout_p
             _lowerCamelCase	:		Optional[Any]          =eps
             _lowerCamelCase	:		Tuple          =use_cache
             _lowerCamelCase	:		int          =vocab_size
             _lowerCamelCase	:		Union[str, Any]          =init_std






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          =np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]					)
       _lowerCamelCase	:		Dict          =np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]					)

       _lowerCamelCase	:		Optional[Any]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       _lowerCamelCase	:		Optional[int]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       # modify the pix within [0, 255]
       _lowerCamelCase	:		Optional[int]          =dst_x * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Any          =dst_y * 255 / np.max(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Tuple          =np.sqrt((np.square(SCREAMING_SNAKE_CASE__					)) + (np.square(SCREAMING_SNAKE_CASE__					))					)
       _lowerCamelCase	:		int          =dst_xy * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		int          =dst_xy.astype(np.uinta					)

       _lowerCamelCase	:		Optional[int]          =np.arctana(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       return dst, theta


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

 lowerCamelCase ,    lowerCamelCase						 =	sobel_filter(gray)

 # show result images
 imshow('sobel filter', sobel_grad)
 imshow('sobel theta', sobel_theta)
 waitKey(0)


