
import cva
import numpy as np




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def __init__(  self     :   List[Any]      ,  lowercase     :   float      ,  lowercase     :   int     ):


				'''simple docstring'''


				if k in (0.04, 0.06):
							_snake_case          =      k
							_snake_case          =      window_size
				else:
							raise ValueError('invalid k value'     )



	def __str__(  self     :   int     ):


				'''simple docstring'''


				return str(self.k     )





	def 			A							(  self     :   List[str]      ,  lowercase     :   str     ):


				'''simple docstring'''


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

										_snake_case          =      (wxx * wyy) - (wxy**2)
										_snake_case          =      wxx + wyy
										_snake_case          =      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__":
			_lowerCamelCase	:    List[str]          =      HarrisCorner(0.0_4, 3)
			_lowerCamelCase	:    Optional[Any]          =      edge_detect.detect('''path_to_image''')
			cva.imwrite('''detect.png''', color_img)

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	:    Tuple          =      logging.get_logger(__name__)

_lowerCamelCase	:    Union[str, Any]          =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = "canine"



	def __init__(  self     :   int      ,  lowercase     :   Optional[int]=768      ,  lowercase     :   Tuple=12      ,  lowercase     :   Union[str, Any]=12      ,  lowercase     :   Optional[int]=3_072      ,  lowercase     :   Tuple="gelu"      ,  lowercase     :   Optional[Any]=0.1      ,  lowercase     :   Tuple=0.1      ,  lowercase     :   int=16_384      ,  lowercase     :   Optional[int]=16      ,  lowercase     :   Optional[int]=0.02      ,  lowercase     :   Optional[Any]=1E-12      ,  lowercase     :   Optional[Any]=0      ,  lowercase     :   Dict=0xE000      ,  lowercase     :   Optional[Any]=0xE001      ,  lowercase     :   Union[str, Any]=4      ,  lowercase     :   str=4      ,  lowercase     :   Optional[int]=8      ,  lowercase     :   List[str]=16_384      ,  lowercase     :   Union[str, Any]=128      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


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

				_snake_case          =      max_position_embeddings
				_snake_case          =      hidden_size
				_snake_case          =      num_hidden_layers
				_snake_case          =      num_attention_heads
				_snake_case          =      intermediate_size
				_snake_case          =      hidden_act
				_snake_case          =      hidden_dropout_prob
				_snake_case          =      attention_probs_dropout_prob
				_snake_case          =      initializer_range
				_snake_case          =      type_vocab_size
				_snake_case          =      layer_norm_eps

				# Character config:
				_snake_case          =      downsampling_rate
				_snake_case          =      upsampling_kernel_size
				_snake_case          =      num_hash_functions
				_snake_case          =      num_hash_buckets
				_snake_case          =      local_transformer_stride