



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


a__      :				Any   	=			logging.get_logger(__name__)

a__      :				List[Any]   	=			{
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}



class       UpperCAmelCase__(							lowerCamelCase	):

			'''simple docstring'''




			A    :  int       				=    "fnet"


			def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Optional[int]=3_20_00			,				lowerCAmelCase  :			Optional[int]=7_68			,				lowerCAmelCase  :			Tuple=12			,				lowerCAmelCase  :			Optional[Any]=30_72			,				lowerCAmelCase  :			List[str]="gelu_new"			,				lowerCAmelCase  :			Any=0.1			,				lowerCAmelCase  :			int=5_12			,				lowerCAmelCase  :			Tuple=4			,				lowerCAmelCase  :			Union[str, Any]=0.02			,				lowerCAmelCase  :			Dict=1E-1_2			,				lowerCAmelCase  :			List[str]=False			,				lowerCAmelCase  :			Optional[int]=5_12			,				lowerCAmelCase  :			Tuple=3			,				lowerCAmelCase  :			Any=1			,				lowerCAmelCase  :			Tuple=2			,				**lowerCAmelCase  :			List[str]			,				)	->				Any:




									"""simple docstring"""

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

									lowercase__     =  vocab_size
									lowercase__     =  max_position_embeddings
									lowercase__     =  hidden_size
									lowercase__     =  num_hidden_layers
									lowercase__     =  intermediate_size
									lowercase__     =  hidden_act
									lowercase__     =  hidden_dropout_prob
									lowercase__     =  initializer_range
									lowercase__     =  type_vocab_size
									lowercase__     =  layer_norm_eps
									lowercase__     =  use_tpu_fourier_optimizations
									lowercase__     =  tpu_short_seq_length





import cva
import numpy as np



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			float			,				lowerCAmelCase  :			int)	->				Dict:




         """simple docstring"""

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


   def __str__(						self  :			Tuple)	->				str:




         """simple docstring"""

         return str(self.k)



   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			str)	->				tuple[cva.Mat, list[list[int]]]:




         """simple docstring"""

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

                     lowercase__     =  (wxx * wyy) - (wxy**2)
                     lowercase__     =  wxx + wyy
                     lowercase__     =  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__":
       a__      :				Dict   	=			HarrisCorner(0.0_4, 3)
       a__	,		a__      :				Dict   	=			edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)
