





'''simple docstring'''






from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__UpperCamelCase					    =	{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}




class  _A (					__lowercase		):
		lowercase__:   int				     =   '''ernie_m'''
		lowercase__:   Dict[str, str]				     =   {"dropout": "classifier_dropout", "num_classes": "num_labels"}

		def __init__(   self					:   Optional[int]  ,      __magic_name__					:   int = 25_00_02  ,      __magic_name__					:   int = 7_68  ,      __magic_name__					:   int = 12  ,      __magic_name__					:   int = 12  ,      __magic_name__					:   int = 30_72  ,      __magic_name__					:   str = "gelu"  ,      __magic_name__					:   float = 0.1  ,      __magic_name__					:   float = 0.1  ,      __magic_name__					:   int = 5_14  ,      __magic_name__					:   float = 0.02  ,      __magic_name__					:   int = 1  ,      __magic_name__					:   float = 1E-05  ,      __magic_name__					:   Union[str, Any]=None  ,      __magic_name__					:   List[Any]=False  ,      __magic_name__					:   Union[str, Any]=0.0  ,      **__magic_name__					:   Optional[Any]  ,      )					->    Tuple:


					"""simple docstring"""

					super().__init__(pad_token_id=__magic_name__  ,      **__magic_name__			)
					__snake_case					:      List[Any]							      =     vocab_size
					__snake_case					:      Dict							      =     hidden_size
					__snake_case					:      Optional[Any]							      =     num_hidden_layers
					__snake_case					:      List[Any]							      =     num_attention_heads
					__snake_case					:      Any							      =     intermediate_size
					__snake_case					:      Tuple							      =     hidden_act
					__snake_case					:      Optional[Any]							      =     hidden_dropout_prob
					__snake_case					:      Optional[int]							      =     attention_probs_dropout_prob
					__snake_case					:      Optional[Any]							      =     max_position_embeddings
					__snake_case					:      Optional[Any]							      =     initializer_range
					__snake_case					:      Optional[Any]							      =     layer_norm_eps
					__snake_case					:      Optional[int]							      =     classifier_dropout
					__snake_case					:      Any							      =     is_decoder
					__snake_case					:      Tuple							      =     act_dropout









'''simple docstring'''






import cva
import numpy as np




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   float  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					if k in (0.04, 0.06):
								__snake_case					:      List[str]							      =     k
								__snake_case					:      int							      =     window_size
					else:
								raise ValueError("""invalid k value"""			)

		def __str__(   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					return str(self.k			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    tuple[cva.Mat, list[list[int]]]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     cva.imread(__magic_name__  ,      0			)
					__snake_case      ,     __snake_case					:      List[str]							      =     img.shape
					__snake_case					:      list[list[int]]							      =     []
					__snake_case					:      str							      =     img.copy()
					__snake_case					:      Tuple							      =     cva.cvtColor(__magic_name__  ,      cva.COLOR_GRAY2RGB			)
					__snake_case      ,     __snake_case					:      List[Any]							      =     np.gradient(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     dx**2
					__snake_case					:      Tuple							      =     dy**2
					__snake_case					:      List[Any]							      =     dx * dy
					__snake_case					:      List[Any]							      =     0.04
					__snake_case					:      Tuple							      =     self.window_size // 2
					for y in range(__magic_name__  ,      h - offset			):
								for x in range(__magic_name__  ,      w - offset			):
											__snake_case					:      Dict							      =     ixx[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      Optional[int]							      =     iyy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      str							      =     ixy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()

											__snake_case					:      List[str]							      =     (wxx * wyy) - (wxy**2)
											__snake_case					:      Dict							      =     wxx + wyy
											__snake_case					:      List[str]							      =     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_55			)
					return color_img, corner_list


if __name__ == "__main__":
	__UpperCamelCase					    =	HarrisCorner(0.04, 3)
	__UpperCamelCase    ,		__UpperCamelCase					    =	edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)


