






import math
import sys

import cva
import numpy as np



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      np.ndarray      ,					__lowercase :      float     )    ->   np.ndarray:





    """simple docstring"""

    __A       =						math.sqrt(__lowercase     )
    __A       =						1 / (sigma * math.sqrt(2 * math.pi     ))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      np.ndarray      ,					__lowercase :      int      ,					__lowercase :      int      ,					__lowercase :      int     )    ->   np.ndarray:





    """simple docstring"""

    __A       =						kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      float     )    ->   np.ndarray:





    """simple docstring"""

    __A       =						np.zeros((kernel_size, kernel_size)     )
    for i in range(0      ,					__lowercase     ):
        for j in range(0      ,					__lowercase     ):
            __A       =						math.sqrt(
                abs(i - kernel_size // 2     ) ** 2 + abs(j - kernel_size // 2     ) ** 2     )
    return vec_gaussian(__lowercase      ,					__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      np.ndarray      ,					__lowercase :      float      ,					__lowercase :      float      ,					__lowercase :      int      ,					)    ->   np.ndarray:





    """simple docstring"""

    __A       =						np.zeros(img.shape     )
    __A       =						get_gauss_kernel(__lowercase      ,					__lowercase     )
    __A     ,						__A       =						img.shape
    for i in range(kernel_size // 2      ,					size_x - kernel_size // 2     ):
        for j in range(kernel_size // 2      ,					size_y - kernel_size // 2     ):
            __A       =						get_slice(__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase     )
            __A       =						img_s - img_s[kernel_size // 2, kernel_size // 2]
            __A       =						vec_gaussian(__lowercase      ,					__lowercase     )
            __A       =						np.multiply(__lowercase      ,					__lowercase     )
            __A       =						np.multiply(__lowercase      ,					__lowercase     )
            __A       =						np.sum(__lowercase     ) / np.sum(__lowercase     )
            __A       =						val
    return imga



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list     )    ->   tuple:





    """simple docstring"""

    __A       =						args[1] if args[1:] else """../image_data/lena.jpg"""
    __A       =						float(args[2]     ) if args[2:] else 1.0
    __A       =						float(args[3]     ) if args[3:] else 1.0
    if args[4:]:
        __A       =						int(args[4]     )
        __A       =						kernel_size + abs(kernel_size % 2 - 1     )
    else:
        __A       =						5
    return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
  __a	,__a	,__a	,__a   :							Any						= parse_args(sys.argv)
  __a   :							Optional[Any]						= cva.imread(filename, 0)
  cva.imshow("input image", img)

  __a   :							List[Any]						= img / 255
  __a   :							Union[str, Any]						= out.astype("float32")
  __a   :							List[str]						= bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
  __a   :							Tuple						= out * 255
  __a   :							Dict						= np.uinta(out)
  cva.imshow("output image", out)
  cva.waitKey(0)
  cva.destroyAllWindows()












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


__a   :							Dict						= logging.get_logger(__name__)

__a   :							Dict						= {
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"gpt_bigcode"
     SCREAMING_SNAKE_CASE     =					["past_key_values"]
     SCREAMING_SNAKE_CASE     =					{
         "hidden_size": "n_embd",
         "max_position_embeddings": "n_positions",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      Dict=50_257			,						UpperCamelCase_							:      int=1_024			,						UpperCamelCase_							:      List[Any]=768			,						UpperCamelCase_							:      str=12			,						UpperCamelCase_							:      Union[str, Any]=12			,						UpperCamelCase_							:      Optional[Any]=None			,						UpperCamelCase_							:      Tuple="gelu_pytorch_tanh"			,						UpperCamelCase_							:      Optional[Any]=0.1			,						UpperCamelCase_							:      List[str]=0.1			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      int=1e-5			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      Dict=50_256			,						UpperCamelCase_							:      Optional[Any]=50_256			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      List[Any]=True			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						vocab_size
         __A       =						n_positions
         __A       =						n_embd
         __A       =						n_layer
         __A       =						n_head
         __A       =						n_inner
         __A       =						activation_function
         __A       =						resid_pdrop
         __A       =						embd_pdrop
         __A       =						attn_pdrop
         __A       =						layer_norm_epsilon
         __A       =						initializer_range
         __A       =						scale_attn_weights
         __A       =						use_cache
         __A       =						attention_softmax_in_fpaa
         __A       =						scale_attention_softmax_in_fpaa
         __A       =						multi_query

         __A       =						bos_token_id
         __A       =						eos_token_id

         super().__init__(bos_token_id=UpperCamelCase_			,						eos_token_id=UpperCamelCase_			,						**UpperCamelCase_					)




