

'''simple docstring'''



from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros

def   lowerCamelCase__       (      a__			,      a__)		->    Dict:



    """simple docstring"""







    _snake_case					,							_snake_case : int												=		image.shape
    _snake_case : Union[str, Any]												=		cols - block_size[1] + 1
    _snake_case : List[Any]												=		rows - block_size[0] + 1
    _snake_case : Dict												=		zeros((dst_height * dst_width, block_size[1] * block_size[0]))
    _snake_case : Tuple												=		0
    for i in range(0			,      a__):
        for j in range(0			,      a__):
            _snake_case : Union[str, Any]												=		ravel(image[i : i + block_size[0], j : j + block_size[1]])
            _snake_case : int												=		window
            row += 1

    return image_array

def   lowerCamelCase__       (      a__			,      a__)		->    Tuple:



    """simple docstring"""







    _snake_case					,							_snake_case : Optional[Any]												=		image.shape[0], image.shape[1]
    _snake_case : List[Any]												=		filter_kernel.shape[0]
    _snake_case : int												=		k_size // 2
    # Pads image with the edge values of array.
    _snake_case : Dict												=		pad(a__			,      a__			,      mode='edge')

    # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
    _snake_case : List[Any]												=		imacol(a__			,      (k_size, k_size))

    #  turn the kernel into shape(k*k, 1)
    _snake_case : int												=		ravel(a__)
    # reshape and get the dst image
    _snake_case : List[Any]												=		dot(a__			,      a__).reshape(a__			,      a__)
    return dst


if __name__ == "__main__":
   # read original image
   SCREAMING_SNAKE_CASE_    =   imread(R"../image_data/lena.jpg")
   # turn image in gray scale value
   SCREAMING_SNAKE_CASE_    =   cvtColor(img, COLOR_BGR2GRAY)
   # Laplace operator
   SCREAMING_SNAKE_CASE_    =   array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
   SCREAMING_SNAKE_CASE_    =   img_convolve(gray, Laplace_kernel).astype(uinta)
   imshow("Laplacian", out)
   waitKey(0)






'''simple docstring'''



import copy

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


SCREAMING_SNAKE_CASE_    =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    =   {}

class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				'''fsmt_decoder'''




      def __init__(					self :		Any     ,					snake_case :		int=0     ,					snake_case :		Dict=0   ):



          """simple docstring"""





          super().__init__()
          _snake_case : int												=		vocab_size
          _snake_case : Dict												=		bos_token_id



class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				'''fsmt'''
      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




      def __init__(					self :		Optional[int]     ,					snake_case :		Tuple=["en", "de"]     ,					snake_case :		Optional[int]=42024     ,					snake_case :		List[str]=42024     ,					snake_case :		Union[str, Any]="relu"     ,					snake_case :		Optional[Any]=1024     ,					snake_case :		Union[str, Any]=200     ,					snake_case :		int=1024     ,					snake_case :		str=4096     ,					snake_case :		Dict=12     ,					snake_case :		str=16     ,					snake_case :		List[str]=0.0     ,					snake_case :		List[Any]=4096     ,					snake_case :		str=12     ,					snake_case :		str=16     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		str=0.1     ,					snake_case :		Tuple=0.0     ,					snake_case :		Optional[int]=0.02     ,					snake_case :		List[str]=2     ,					snake_case :		int=True     ,					snake_case :		Any=True     ,					snake_case :		str=False     ,					snake_case :		Union[str, Any]=5     ,					snake_case :		Dict=1.0     ,					snake_case :		Union[str, Any]=False     ,					snake_case :		Optional[Any]=True     ,					snake_case :		Optional[int]=1     ,					snake_case :		int=0     ,					snake_case :		Optional[int]=2     ,					snake_case :		Optional[Any]=2     ,					**snake_case :		Any     ,					):



          """simple docstring"""





          _snake_case : int												=		langs
          _snake_case : str												=		src_vocab_size
          _snake_case : List[Any]												=		tgt_vocab_size
          _snake_case : Optional[int]												=		d_model  # encoder_embed_dim and decoder_embed_dim

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

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

          _snake_case : str												=		scale_embedding  # scale factor will be sqrt(d_model) if True

          # 3 Types of Dropout
          _snake_case : int												=		attention_dropout
          _snake_case : List[Any]												=		activation_dropout
          _snake_case : Optional[int]												=		dropout

          _snake_case : Union[str, Any]												=		use_cache
          super().__init__(
              pad_token_id=snake_case     ,					bos_token_id=snake_case     ,					eos_token_id=snake_case     ,					decoder_start_token_id=snake_case     ,					is_encoder_decoder=snake_case     ,					tie_word_embeddings=snake_case     ,					forced_eos_token_id=snake_case     ,					max_length=snake_case     ,					num_beams=snake_case     ,					length_penalty=snake_case     ,					early_stopping=snake_case     ,					**snake_case     ,					)




      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Any												=		copy.deepcopy(self.__dict__   )
          _snake_case : str												=		self.decoder.to_dict()
          _snake_case : Dict												=		self.__class__.model_type
          return output



