
'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}

class     a (					__magic_name__     ):
				_snake_case		     =			MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
				_snake_case		     =			'''mobilebert'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					int=3_05_22,     SCREAMING_SNAKE_CASE_						:					List[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					str=24,     SCREAMING_SNAKE_CASE_						:					List[Any]=4,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					int="relu",     SCREAMING_SNAKE_CASE_						:					Tuple=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					int=2,     SCREAMING_SNAKE_CASE_						:					Dict=0.02,     SCREAMING_SNAKE_CASE_						:					List[str]=1e-12,     SCREAMING_SNAKE_CASE_						:					List[Any]=0,     SCREAMING_SNAKE_CASE_						:					Any=1_28,     SCREAMING_SNAKE_CASE_						:					Tuple=True,     SCREAMING_SNAKE_CASE_						:					Dict=True,     SCREAMING_SNAKE_CASE_						:					List[str]=1_28,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=False,     SCREAMING_SNAKE_CASE_						:					List[Any]=True,     SCREAMING_SNAKE_CASE_						:					Any=4,     SCREAMING_SNAKE_CASE_						:					Dict="no_norm",     SCREAMING_SNAKE_CASE_						:					Any=True,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     vocab_size
										snake_case							:  Optional[Any]			=     hidden_size
										snake_case							:  Union[str, Any]			=     num_hidden_layers
										snake_case							:  str			=     num_attention_heads
										snake_case							:  Tuple			=     hidden_act
										snake_case							:  Optional[int]			=     intermediate_size
										snake_case							:  str			=     hidden_dropout_prob
										snake_case							:  Any			=     attention_probs_dropout_prob
										snake_case							:  Dict			=     max_position_embeddings
										snake_case							:  List[str]			=     type_vocab_size
										snake_case							:  int			=     initializer_range
										snake_case							:  List[Any]			=     layer_norm_eps
										snake_case							:  Any			=     embedding_size
										snake_case							:  Tuple			=     trigram_input
										snake_case							:  str			=     use_bottleneck
										snake_case							:  Union[str, Any]			=     intra_bottleneck_size
										snake_case							:  Any			=     use_bottleneck_attention
										snake_case							:  Tuple			=     key_query_shared_bottleneck
										snake_case							:  int			=     num_feedforward_networks
										snake_case							:  Union[str, Any]			=     normalization_type
										snake_case							:  Union[str, Any]			=     classifier_activation

										if self.use_bottleneck:
																snake_case							:  Optional[int]			=     intra_bottleneck_size
										else:
																snake_case							:  Optional[int]			=     hidden_size

										snake_case							:  Optional[Any]			=     classifier_dropout




class     a (					__magic_name__     ):




				@property
				def 						__snake_case				(				self						:					List[str]   ):
										if self.task == "multiple-choice":
																snake_case							:  Any			=     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																snake_case							:  List[str]			=     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										        ('''token_type_ids''', dynamic_axis),
										    ]   )




"""simple docstring"""



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





def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       List[Any]=3      ):
     A__				      =	int(mask / 2      )
     # copy image size
     A__				      =	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__				      =	ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
               # calculate mask median
               A__				      =	sort(UpperCAmelCase_      )[inta(divide((multiply(UpperCAmelCase_       , UpperCAmelCase_      ))       , 2      ) + 1      )]
               A__				      =	median
     return median_img


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

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

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






