



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		k_size // 2
 a		,  a						=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
 a						=		1 / (2 * pi * sigma) * exp(-(square(UpperCAmelCase__					) + square(UpperCAmelCase__					)) / (2 * square(UpperCAmelCase__					))					)
 return g


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str ,					UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :Optional[Any]					):






 '''simple docstring'''

 a		,  a						=		image.shape[0], image.shape[1]
 # dst image height and width
 a						=		height - k_size + 1
 a						=		width - k_size + 1

 # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
 a						=		zeros((dst_height * dst_width, k_size * k_size)					)
 a						=		0
 for i, j in product(range(UpperCAmelCase__					) ,					range(UpperCAmelCase__					)					):
  a						=		ravel(image[i : i + k_size, j : j + k_size]					)
  a						=		window
  row += 1

 #  turn the kernel into shape(k*k, 1)
 a						=		gen_gaussian_kernel(UpperCAmelCase__ ,					UpperCAmelCase__					)
 a						=		ravel(UpperCAmelCase__					)

 # reshape and get the dst image
 a						=		dot(UpperCAmelCase__ ,					UpperCAmelCase__					).reshape(UpperCAmelCase__ ,					UpperCAmelCase__					).astype(UpperCAmelCase__					)

 return dst


if __name__ == "__main__":
     # read original image
     A_		:     int        =     imread(r'''../image_data/lena.jpg''')
     # turn image in gray scale value
     A_		:     str        =     cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     A_		:     List[str]        =     gaussian_filter(gray, 3, sigma=1)
     A_		:     Optional[int]        =     gaussian_filter(gray, 5, sigma=0.8)

     # show result images
     imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
     imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
     waitKey()






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


A_		:     str        =     logging.get_logger(__name__)

A_		:     List[Any]        =     {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''rwkv'''
   _UpperCAmelCase     	=			{'''max_position_embeddings''': '''context_length'''}

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       Union[str, Any]=5_0277							,   __lowerCAmelCase			:       str=1024							,   __lowerCAmelCase			:       Union[str, Any]=4096							,   __lowerCAmelCase			:       Optional[int]=32							,   __lowerCAmelCase			:       Optional[int]=None							,   __lowerCAmelCase			:       Optional[Any]=None							,   __lowerCAmelCase			:       List[Any]=1E-5							,   __lowerCAmelCase			:       Union[str, Any]=0							,   __lowerCAmelCase			:       Optional[Any]=0							,   __lowerCAmelCase			:       Dict=6							,   __lowerCAmelCase			:       int=False							,   __lowerCAmelCase			:       Tuple=True							,   **__lowerCAmelCase			:       List[str]							,   )       ->				List[Any]:


    """simple docstring"""






    a						=		vocab_size
    a						=		context_length
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		attention_hidden_size if attention_hidden_size is not None else hidden_size
    a						=		intermediate_size if intermediate_size is not None else 4 * hidden_size
    a						=		layer_norm_epsilon
    a						=		rescale_every
    a						=		use_cache

    a						=		bos_token_id
    a						=		eos_token_id

    super().__init__(
        tie_word_embeddings=__lowerCAmelCase							,   bos_token_id=__lowerCAmelCase							,   eos_token_id=__lowerCAmelCase							,   **__lowerCAmelCase							)

