


import cva
import numpy as np







class        UpperCamelCase__   :

					def __init__(     self       :   List[str]							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






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

					def __str__(     self       :   Optional[int]       ):






										'''simple docstring'''






										return str(self.k       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			cva.imread(UpperCamelCase__							,					0       )
										lowercase_							,					lowercase_  							=			img.shape
										lowercase_  							=			[]
										lowercase_  							=			img.copy()
										lowercase_  							=			cva.cvtColor(UpperCamelCase__							,					cva.COLOR_GRAY2RGB       )
										lowercase_							,					lowercase_  							=			np.gradient(UpperCamelCase__       )
										lowercase_  							=			dx**2
										lowercase_  							=			dy**2
										lowercase_  							=			dx * dy
										lowercase_  							=			0.04
										lowercase_  							=			self.window_size // 2
										for y in range(UpperCamelCase__							,					h - offset       ):
															for x in range(UpperCamelCase__							,					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)							,					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)






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				__magic_name__   ):

					@register_to_config
					def __init__(     self       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   bool = False							,					):






										'''simple docstring'''






										super().__init__()

										lowercase_  							=			nn.Embedding(UpperCamelCase__							,					UpperCamelCase__       )

										lowercase_  							=			nn.Embedding(UpperCamelCase__							,					UpperCamelCase__       )
										lowercase_  							=			False

										lowercase_  							=			nn.Dropout(p=UpperCamelCase__       )

										lowercase_  							=			TaConfig(
										    vocab_size=UpperCamelCase__							,					d_model=UpperCamelCase__							,					num_heads=UpperCamelCase__							,					d_kv=UpperCamelCase__							,					d_ff=UpperCamelCase__							,					dropout_rate=UpperCamelCase__							,					feed_forward_proj=UpperCamelCase__							,					is_decoder=UpperCamelCase__							,					is_encoder_decoder=UpperCamelCase__							,					)

										lowercase_  							=			nn.ModuleList()
										for lyr_num in range(UpperCamelCase__       ):
															lowercase_  							=			TaBlock(UpperCamelCase__       )
															self.encoders.append(UpperCamelCase__       )

										lowercase_  							=			TaLayerNorm(UpperCamelCase__       )
										lowercase_  							=			nn.Dropout(p=UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.token_embedder(UpperCamelCase__       )

										lowercase_  							=			encoder_input_tokens.shape[1]
										lowercase_  							=			torch.arange(UpperCamelCase__							,					device=encoder_input_tokens.device       )
										x += self.position_encoding(UpperCamelCase__       )

										lowercase_  							=			self.dropout_pre(UpperCamelCase__       )

										# inverted the attention mask
										lowercase_  							=			encoder_input_tokens.size()
										lowercase_  							=			self.get_extended_attention_mask(UpperCamelCase__							,					UpperCamelCase__       )

										for lyr in self.encoders:
															lowercase_  							=			lyr(UpperCamelCase__							,					UpperCamelCase__       )[0]
										lowercase_  							=			self.layer_norm(UpperCamelCase__       )

										return self.dropout_post(UpperCamelCase__       ), encoder_inputs_mask


