




'''simple docstring'''





import copy

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


a		:  int   			=			logging.get_logger(__name__)

a		:  Dict   			=			{}




class     UpperCamelCase					(  snake_case__					):
  a    :Tuple      					=				'fsmt_decoder'



  def __init__( self		,    __lowerCAmelCase=0		,    __lowerCAmelCase=0   ):







      '''simple docstring'''


      super().__init__()
      _a							:       List[str]       					=			vocab_size
      _a							:       str       					=			bos_token_id






class     UpperCamelCase					(  snake_case__					):
  a    :Union[str, Any]      					=				'fsmt'
  a    :Union[str, Any]      					=				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



  def __init__( self		,    __lowerCAmelCase=["en", "de"]		,    __lowerCAmelCase=4_2024		,    __lowerCAmelCase=4_2024		,    __lowerCAmelCase="relu"		,    __lowerCAmelCase=1024		,    __lowerCAmelCase=200		,    __lowerCAmelCase=1024		,    __lowerCAmelCase=4096		,    __lowerCAmelCase=12		,    __lowerCAmelCase=16		,    __lowerCAmelCase=0.0		,    __lowerCAmelCase=4096		,    __lowerCAmelCase=12		,    __lowerCAmelCase=16		,    __lowerCAmelCase=0.0		,    __lowerCAmelCase=0.0		,    __lowerCAmelCase=0.1		,    __lowerCAmelCase=0.0		,    __lowerCAmelCase=0.02		,    __lowerCAmelCase=2		,    __lowerCAmelCase=True		,    __lowerCAmelCase=True		,    __lowerCAmelCase=False		,    __lowerCAmelCase=5		,    __lowerCAmelCase=1.0		,    __lowerCAmelCase=False		,    __lowerCAmelCase=True		,    __lowerCAmelCase=1		,    __lowerCAmelCase=0		,    __lowerCAmelCase=2		,    __lowerCAmelCase=2		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       List[Any]       					=			langs
      _a							:       Dict       					=			src_vocab_size
      _a							:       str       					=			tgt_vocab_size
      _a							:       str       					=			d_model  # encoder_embed_dim and decoder_embed_dim

      _a							:       str       					=			encoder_ffn_dim
      _a							:       str       					=			encoder_layers
      _a							:       List[str]       					=			encoder_attention_heads
      _a							:       Tuple       					=			encoder_layerdrop
      _a							:       Optional[Any]       					=			decoder_layerdrop
      _a							:       Dict       					=			decoder_ffn_dim
      _a							:       Optional[int]       					=			decoder_layers
      _a							:       Dict       					=			decoder_attention_heads
      _a							:       int       					=			max_position_embeddings
      _a							:       List[str]       					=			init_std  # Normal(0, this parameter)
      _a							:       Optional[Any]       					=			activation_function

      _a							:       Any       					=			DecoderConfig(vocab_size=__lowerCAmelCase		,    bos_token_id=__lowerCAmelCase   )
      if "decoder" in common_kwargs:
          del common_kwargs["decoder"]

      _a							:       Dict       					=			scale_embedding  # scale factor will be sqrt(d_model) if True

      # 3 Types of Dropout
      _a							:       Optional[Any]       					=			attention_dropout
      _a							:       str       					=			activation_dropout
      _a							:       Union[str, Any]       					=			dropout

      _a							:       List[Any]       					=			use_cache
      super().__init__(
          pad_token_id=__lowerCAmelCase		,    bos_token_id=__lowerCAmelCase		,    eos_token_id=__lowerCAmelCase		,    decoder_start_token_id=__lowerCAmelCase		,    is_encoder_decoder=__lowerCAmelCase		,    tie_word_embeddings=__lowerCAmelCase		,    forced_eos_token_id=__lowerCAmelCase		,    max_length=__lowerCAmelCase		,    num_beams=__lowerCAmelCase		,    length_penalty=__lowerCAmelCase		,    early_stopping=__lowerCAmelCase		,    **__lowerCAmelCase		,    )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[Any]       					=			copy.deepcopy(self.__dict__   )
      _a							:       str       					=			self.decoder.to_dict()
      _a							:       Optional[Any]       					=			self.__class__.model_type
      return output








'''simple docstring'''





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





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


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

  # get values with two different mask size
  a		:  str   			=			median_filter(gray, 3)
  a		:  Optional[Any]   			=			median_filter(gray, 5)

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


