


from ... import PretrainedConfig


snake_case							:       str     			=      {
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}







class        snake_case_   (a_      ):
					UpperCAmelCase__					:    List[str]          =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
					UpperCAmelCase__					:    Dict          =			'''nezha'''
					def __init__(      self   :Tuple			,__snake_case   :int=2_11_28			,__snake_case   :Union[str, Any]=7_68			,__snake_case   :Union[str, Any]=12			,__snake_case   :int=12			,__snake_case   :int=30_72			,__snake_case   :Dict="gelu"			,__snake_case   :Union[str, Any]=0.1			,__snake_case   :Any=0.1			,__snake_case   :str=5_12			,__snake_case   :Any=64			,__snake_case   :Union[str, Any]=2			,__snake_case   :List[str]=0.02			,__snake_case   :str=1E-12			,__snake_case   :Dict=0.1			,__snake_case   :str=0			,__snake_case   :str=2			,__snake_case   :List[Any]=3			,__snake_case   :Any=True			,**__snake_case   :Optional[int]			,)					->    List[Any]:
												super().__init__(pad_token_id=_A			,bos_token_id=_A			,eos_token_id=_A			,**_A      )

												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__             =     max_relative_position
												a__             =     type_vocab_size
												a__             =     initializer_range
												a__             =     layer_norm_eps
												a__             =     classifier_dropout
												a__             =     use_cache











import cva
import numpy as np

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				if k in (0.04, 0.06):
							UpperCAmelCase   		=				k
							UpperCAmelCase   		=				window_size
				else:
							raise ValueError('''invalid k value'''		)
	def __str__(    self		):






				'''simple docstring'''







				return str(self.k		)







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				cva.imread(_A							,					0		)
				UpperCAmelCase		,			UpperCAmelCase   		=				img.shape
				UpperCAmelCase   		=				[]
				UpperCAmelCase   		=				img.copy()
				UpperCAmelCase   		=				cva.cvtColor(_A							,					cva.COLOR_GRAY2RGB		)
				UpperCAmelCase		,			UpperCAmelCase   		=				np.gradient(_A		)
				UpperCAmelCase   		=				dx**2
				UpperCAmelCase   		=				dy**2
				UpperCAmelCase   		=				dx * dy
				UpperCAmelCase   		=				0.04
				UpperCAmelCase   		=				self.window_size // 2
				for y in range(_A							,					h - offset		):
							for x in range(_A							,					w - offset		):
										UpperCAmelCase   		=				ixx[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										UpperCAmelCase   		=				iyy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										UpperCAmelCase   		=				ixy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()

										UpperCAmelCase   		=				(wxx * wyy) - (wxy**2)
										UpperCAmelCase   		=				wxx + wyy
										UpperCAmelCase   		=				det - k * (trace**2)
										# Can change the value
										if r > 0.5:
													corner_list.append([x, y, r]		)
													color_img.itemset((y, x, 0)							,					0		)
													color_img.itemset((y, x, 1)							,					0		)
													color_img.itemset((y, x, 2)							,					2_5_5		)
				return color_img, corner_list


if __name__ == "__main__":
				__A				:    Tuple       =   HarrisCorner(0.04, 3)
				__A    , __A				:    List[Any]       =   edge_detect.detect("path_to_image")
				cva.imwrite("detect.png", color_img)



