





"""simple docstring"""
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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    Any			):
 lowerCAmelCase    				= k_size // 2
 lowerCAmelCase				,lowerCAmelCase    				= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
 lowerCAmelCase    				= 1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase			) + square(_UpperCAmelCase			)) / (2 * square(_UpperCAmelCase			))			)
 return g

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    Optional[Any]			):
 lowerCAmelCase				,lowerCAmelCase    				= image.shape[0], image.shape[1]
 # dst image height and width
 lowerCAmelCase    				= height - k_size + 1
 lowerCAmelCase    				= width - k_size + 1

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

 #  turn the kernel into shape(k*k, 1)
 lowerCAmelCase    				= gen_gaussian_kernel(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= ravel(_UpperCAmelCase			)

 # reshape and get the dst image
 lowerCAmelCase    				= dot(_UpperCAmelCase  ,     _UpperCAmelCase			).reshape(_UpperCAmelCase  ,     _UpperCAmelCase			).astype(_UpperCAmelCase			)

 return dst


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

       # get values with two different mask size
       __UpperCamelCase     :						Optional[Any]						       =  gaussian_filter(gray, 3, sigma=1)
       __UpperCamelCase     :						Tuple						       =  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()






'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase    :     Optional[Any]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     List[Any]         =       {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "roberta-prelayernorm"
   def __init__(      self      :			Optional[int], UpperCamelCase__      :			List[Any]=5_02_65, UpperCamelCase__      :			int=7_68, UpperCamelCase__      :			str=12, UpperCamelCase__      :			Union[str, Any]=12, UpperCamelCase__      :			Tuple=30_72, UpperCamelCase__      :			Tuple="gelu", UpperCamelCase__      :			List[Any]=0.1, UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			Any=5_12, UpperCamelCase__      :			Union[str, Any]=2, UpperCamelCase__      :			Optional[Any]=0.02, UpperCamelCase__      :			str=1e-12, UpperCamelCase__      :			str=1, UpperCamelCase__      :			List[str]=0, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Optional[Any]="absolute", UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			List[str]=None, **UpperCamelCase__      :			Any, )  ->			Dict:
        super().__init__(pad_token_id=UpperCamelCase__, bos_token_id=UpperCamelCase__, eos_token_id=UpperCamelCase__, **UpperCamelCase__     )

        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  hidden_act
        _A    		=  intermediate_size
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  initializer_range
        _A    		=  layer_norm_eps
        _A    		=  position_embedding_type
        _A    		=  use_cache
        _A    		=  classifier_dropout







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
             _A    		=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             _A    		=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]     )



