






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


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    '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_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   "pegasus_x"
 _SCREAMING_SNAKE_CASE							:     Tuple	=   ["past_key_values"]
 _SCREAMING_SNAKE_CASE							:     List[Any]	=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


 def __init__(		self							,					_A=96103							,					_A=16384							,					_A=16							,					_A=4096							,					_A=16							,					_A=16							,					_A=4096							,					_A=16							,					_A=0.0							,					_A=0.0							,					_A=True							,					_A=True							,					_A="gelu"							,					_A=1024							,					_A=0.1							,					_A=0.0							,					_A=0.0							,					_A=0.02							,					_A=0							,					_A=True							,					_A=0							,					_A=1							,					_A=1							,					_A=32							,					_A=512							,					_A=True							,					**_A							,					)	->						int:

     """simple docstring"""



     _UpperCAmelCase			: int     					=			vocab_size
     _UpperCAmelCase			: int     					=			max_position_embeddings
     _UpperCAmelCase			: List[str]     					=			d_model
     _UpperCAmelCase			: Dict     					=			encoder_ffn_dim
     _UpperCAmelCase			: List[Any]     					=			encoder_layers
     _UpperCAmelCase			: Optional[int]     					=			encoder_attention_heads
     _UpperCAmelCase			: int     					=			decoder_ffn_dim
     _UpperCAmelCase			: Union[str, Any]     					=			decoder_layers
     _UpperCAmelCase			: List[str]     					=			decoder_attention_heads
     _UpperCAmelCase			: List[str]     					=			dropout
     _UpperCAmelCase			: List[str]     					=			attention_dropout
     _UpperCAmelCase			: Tuple     					=			activation_dropout
     _UpperCAmelCase			: int     					=			activation_function
     _UpperCAmelCase			: Optional[Any]     					=			init_std
     _UpperCAmelCase			: List[str]     					=			encoder_layerdrop
     _UpperCAmelCase			: Dict     					=			decoder_layerdrop
     _UpperCAmelCase			: List[str]     					=			use_cache
     _UpperCAmelCase			: Optional[int]     					=			encoder_layers
     _UpperCAmelCase			: Dict     					=			scale_embedding  # scale factor will be sqrt(d_model) if True

     _UpperCAmelCase			: List[Any]     					=			num_global_tokens
     _UpperCAmelCase			: List[Any]     					=			block_size
     _UpperCAmelCase			: str     					=			stagger_local_blocks

     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 	snake_case__			(		self)	->						int:

     """simple docstring"""



     return self.encoder_attention_heads


 @property
 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     return self.d_model












from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def        _lowerCamelCase		(  __A      :							str						,						__A      :							List[str]=3	) ->   List[Any]:
    _UpperCAmelCase			: Tuple     					=			int(mask / 2	)
    # copy image size
    _UpperCAmelCase			: Any     					=			zeros_like(__A	)
    for i in range(__A						,						gray_img.shape[0] - bd	):
        for j in range(__A						,						gray_img.shape[1] - bd	):
            # get mask according with mask
            _UpperCAmelCase			: Any     					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _UpperCAmelCase			: List[str]     					=			sort(__A	)[inta(divide((multiply(__A						,						__A	))						,						2	) + 1	)]
            _UpperCAmelCase			: int     					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  SCREAMING_SNAKE_CASE								=  imread('../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								=  median_filter(gray, 3)
  SCREAMING_SNAKE_CASE								=  median_filter(gray, 5)

  # show result images
  imshow('median filter with 3x3 mask', medianaxa)
  imshow('median filter with 5x5 mask', medianaxa)
  waitKey(0)




