






"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


A_      =							logging.get_logger(__name__)

A_      =							{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):
							snake_case_       =							'convbert'


							def __init__(     self :  Optional[int]     ,      snake_case :  Optional[Any]=3_0522     ,      snake_case :  List[Any]=768     ,      snake_case :  Tuple=12     ,      snake_case :  Any=12     ,      snake_case :  str=3072     ,      snake_case :  Optional[int]="gelu"     ,      snake_case :  Any=0.1     ,      snake_case :  int=0.1     ,      snake_case :  Dict=512     ,      snake_case :  Optional[Any]=2     ,      snake_case :  Optional[int]=0.02     ,      snake_case :  Optional[int]=1e-12     ,      snake_case :  str=1     ,      snake_case :  Optional[Any]=0     ,      snake_case :  str=2     ,      snake_case :  int=768     ,      snake_case :  Union[str, Any]=2     ,      snake_case :  Optional[int]=9     ,      snake_case :  Optional[int]=1     ,      snake_case :  str=None     ,      **snake_case :  List[Any]     ,      ):
													'''simple docstring'''





													super().__init__(
													    pad_token_id=snake_case     ,      bos_token_id=snake_case     ,      eos_token_id=snake_case     ,      **snake_case     ,      )

													A__  :					Dict											=					vocab_size
													A__  :					Dict											=					hidden_size
													A__  :					int											=					num_hidden_layers
													A__  :					Any											=					num_attention_heads
													A__  :					Dict											=					intermediate_size
													A__  :					Union[str, Any]											=					hidden_act
													A__  :					List[Any]											=					hidden_dropout_prob
													A__  :					Tuple											=					attention_probs_dropout_prob
													A__  :					Optional[Any]											=					max_position_embeddings
													A__  :					Dict											=					type_vocab_size
													A__  :					Optional[int]											=					initializer_range
													A__  :					Union[str, Any]											=					layer_norm_eps
													A__  :					str											=					embedding_size
													A__  :					int											=					head_ratio
													A__  :					Optional[Any]											=					conv_kernel_size
													A__  :					List[str]											=					num_groups
													A__  :					Union[str, Any]											=					classifier_dropout






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):


							@property
							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





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














"""simple docstring"""



import cva
import numpy as np






class 							__SCREAMING_SNAKE_CASE  :


							def __init__(     self :  Union[str, Any]     ,      snake_case :  float     ,      snake_case :  int							):
													'''simple docstring'''





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


							def __str__(     self :  List[Any]							):
													'''simple docstring'''





													return str(self.k							)


							def   _UpperCamelCase    (     self :  int     ,      snake_case :  str							):
													'''simple docstring'''





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

																									A__  :					int											=					(wxx * wyy) - (wxy**2)
																									A__  :					Any											=					wxx + wyy
																									A__  :					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)     ,      255							)
													return color_img, corner_list


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






