

from typing import Callable, Optional, Union

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


_lowerCamelCase								=logging.get_logger(__name__)

_lowerCamelCase								={
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}



class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				'prophetnet'
      __UpperCAmelCase      		=				['past_key_values']
      __UpperCAmelCase      		=				{
          'num_attention_heads': 'num_encoder_attention_heads',
      }
      def __init__(				self					:							int      ,snake_case					:							Optional[float] = 0.1      ,snake_case					:							Optional[Union[str, Callable]] = "gelu"      ,snake_case					:							Optional[int] = 30522      ,snake_case					:							Optional[int] = 1024      ,snake_case					:							Optional[int] = 4096      ,snake_case					:							Optional[int] = 12      ,snake_case					:							Optional[int] = 16      ,snake_case					:							Optional[int] = 4096      ,snake_case					:							Optional[int] = 12      ,snake_case					:							Optional[int] = 16      ,snake_case					:							Optional[float] = 0.1      ,snake_case					:							Optional[float] = 0.1      ,snake_case					:							Optional[int] = 512      ,snake_case					:							Optional[float] = 0.02      ,snake_case					:							Optional[bool] = True      ,snake_case					:							Optional[bool] = True      ,snake_case					:							Optional[int] = 0      ,snake_case					:							Optional[int] = 2      ,snake_case					:							Optional[int] = 32      ,snake_case					:							Optional[int] = 128      ,snake_case					:							Optional[bool] = False      ,snake_case					:							Optional[float] = 0.0      ,snake_case					:							Optional[bool] = True      ,snake_case					:							Optional[int] = 0      ,snake_case					:							Optional[int] = 1      ,snake_case					:							Optional[int] = 2      ,**snake_case					:							int      ,):
           SCREAMING_SNAKE_CASE										=vocab_size
           SCREAMING_SNAKE_CASE										=hidden_size
           SCREAMING_SNAKE_CASE										=encoder_ffn_dim
           SCREAMING_SNAKE_CASE										=num_encoder_layers
           SCREAMING_SNAKE_CASE										=num_encoder_attention_heads
           SCREAMING_SNAKE_CASE										=decoder_ffn_dim
           SCREAMING_SNAKE_CASE										=num_decoder_layers
           SCREAMING_SNAKE_CASE										=num_decoder_attention_heads
           SCREAMING_SNAKE_CASE										=max_position_embeddings
           SCREAMING_SNAKE_CASE										=init_std  # Normal(0, this parameter)
           SCREAMING_SNAKE_CASE										=activation_function

           # parameters for prophetnet
           SCREAMING_SNAKE_CASE										=ngram
           SCREAMING_SNAKE_CASE										=num_buckets
           SCREAMING_SNAKE_CASE										=relative_max_distance
           SCREAMING_SNAKE_CASE										=disable_ngram_loss
           SCREAMING_SNAKE_CASE										=eps

           # 3 Types of Dropout
           SCREAMING_SNAKE_CASE										=attention_dropout
           SCREAMING_SNAKE_CASE										=activation_dropout
           SCREAMING_SNAKE_CASE										=dropout

           SCREAMING_SNAKE_CASE										=use_cache

           super().__init__(
               pad_token_id=snake_case      ,bos_token_id=snake_case      ,eos_token_id=snake_case      ,is_encoder_decoder=snake_case      ,add_cross_attention=snake_case      ,decoder_start_token_id=snake_case      ,**snake_case      ,)
      @property
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           return self.num_encoder_layers + self.num_decoder_layers


      @num_hidden_layers.setter
      def 	_lowerCAmelCase					(				self					:							Optional[Any]      ,snake_case					:							List[Any]    ):
           raise NotImplementedError(
               'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
               ' `num_decoder_layers`.'    )




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)

