

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


_lowerCamelCase								=logging.get_logger(__name__)

_lowerCamelCase								={
    "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 		a_	(							lowerCamelCase_					):



      """simple docstring"""







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

           SCREAMING_SNAKE_CASE										=num_global_tokens
           SCREAMING_SNAKE_CASE										=block_size
           SCREAMING_SNAKE_CASE										=stagger_local_blocks

           super().__init__(
               pad_token_id=snake_case      ,eos_token_id=snake_case      ,is_encoder_decoder=snake_case      ,decoder_start_token_id=snake_case      ,forced_eos_token_id=snake_case      ,**snake_case      ,)
      @property
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           return self.encoder_attention_heads


      @property
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return self.d_model




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

from digital_image_processing.filters.convolve import img_convolve







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]      )
     SCREAMING_SNAKE_CASE										=np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]      )

     SCREAMING_SNAKE_CASE										=np.abs(img_convolve(lowerCAmelCase_,   lowerCAmelCase_      )      )
     SCREAMING_SNAKE_CASE										=np.abs(img_convolve(lowerCAmelCase_,   lowerCAmelCase_      )      )
     # modify the pix within [0, 255]
     SCREAMING_SNAKE_CASE										=dst_x * 255 / np.max(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=dst_y * 255 / np.max(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=np.sqrt((np.square(lowerCAmelCase_      )) + (np.square(lowerCAmelCase_      ))      )
     SCREAMING_SNAKE_CASE										=dst_xy * 255 / np.max(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=dst_xy.astype(np.uinta      )

     SCREAMING_SNAKE_CASE										=np.arctana(lowerCAmelCase_,   lowerCAmelCase_      )
     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)

